System and method for use of treatment device to reduce pain medication dependency

ABSTRACT

Systems, methods, and computer-readable mediums for modifying, by an artificial intelligence engine, a treatment plan for optimizing patient outcome and pain levels during treatment sessions. The system includes, in one implementation, a treatment apparatus, a patient interface, and a computing device. The treatment apparatus is configured to be manipulated by a patient while the patient performs the treatment sessions. The computing device is configured to receive the treatment plan for the patient and treatment data pertaining to the patient. The computing device is also configured to receive patient input from the patient interface correlating with the pain levels of the patient. The computing device is further configured to use the treatment plan, the treatment data, and the patient input to generate at least one threshold. Responsive to an occurrence of exceeding the at least one threshold, the computing device is configured to modify the treatment plan.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/147,295, filed Jan. 12, 2021, titled “System and Method for Use ofTreatment Device to Reduce Pain Medication Dependency”, which is acontinuation-in-part of U.S. patent application Ser. No. 17/021,895,filed Sep. 15, 2020, titled “Telemedicine for Orthopedic Treatment,”which claims priority to and the benefit of U.S. Provisional PatentApplication Ser. No. 62/910,232, filed Oct. 3, 2019, titled“Telemedicine for Orthopedic Treatment,” the entire disclosures of whichare hereby incorporated by reference for all purposes.

TECHNICAL FIELD

This disclosure relates generally to a system and a method for use of atreatment device to reduce pain medication dependency.

BACKGROUND

The opioid epidemic refers to the growing number of hospitalizations anddeaths caused by people abusing opioids, including prescription drugs,illicit drugs, and analogues drugs. Annually in the United States,approximately 40,000 people die from an accidental overdose of opioids.Opioids, such as morphine, OxyContin, Vicodin, codeine, fentanyl, andthe like, are drugs that are often used to relieve pain. Opioids arehighly addictive drugs and can cause biochemical changes in the brainsof people after continued use. Most people suffering from an opioidaddiction initially began taking the drugs after they received, from adoctor, a prescription for pain medication (e.g., opioids) to alleviatepain resulting from an injury or a surgery. As patients engage inrehabilitation, their pain levels increase, which often leads to thepatients taking more pain medication. In addition, the increased painlevels may discourage the patients from diligently following theirrehabilitation treatment plans. Such noncompliance may slow down therecovery progresses of the patients, leading to patients taking painmedication for longer time periods. As the quantity and the length oftime (e.g., days, weeks, months, etc.) increases for which patients taketheir pain medication, the more likely the patients will become addictedto and/or dependent on opioids. For example, patients may becomephysically dependent on opioids and experience symptoms of tolerance(i.e., patients' bodies become desensitized to the drugs and patientsneed to take higher doses of the drug to relieve pain) and withdrawal(e.g., physical effects). Furthermore, patients may become mentallydependent on the opioids (i.e., the use of the opioids is a conditionedresponse to a feeling—a trigger—and the trigger sets off biochemicalchanges in the patients' brains that strongly influence addictivebehavior).

Remote medical assistance, also referred to, inter alia, as remotemedicine, telemedicine, telemed, telmed, tel-med, or telehealth, is anat least two-way communication between a healthcare professional orprofessionals, such as a physician or a physical therapist, and apatient using audio and/or audiovisual and/or other sensorial orperceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based orelectromagnetic (e.g., neurostimulative)) communications (e.g., via acomputer, a smartphone, or a tablet). The patient may use a patientinterface in communication with an assistant interface for receiving theremote medical assistance via audio, visual, audiovisual, or othercommunications described elsewhere herein. Any reference herein to anyparticular sensorial modality shall be understood to include and todisclose by implication a different one or more sensory modalities.

Doctors typically prescribe opioids to a patient after conducting aphysical examination and/or communicating with the patient (e.g., toobtain the patient's rehabilitative progress and/or pain level).Telemedicine is an option for healthcare professionals to communicatewith patients and provide patient care when the patients do not want toor cannot easily go to the healthcare professionals' offices.Telemedicine, however, has substantive limitations as the healthcareprofessionals cannot conduct physical examinations of the patients.Rather, the healthcare professionals must rely on verbal communicationand/or limited remote observation of the patients.

SUMMARY

In general, the present disclosure provides a system and a method foruse of a treatment device to reduce pain medication dependency.

An aspect of the disclosed embodiments includes a computer-implementedsystem. The computer-implemented system includes, in one example, atreatment apparatus, a patient interface, and a computing device. Thetreatment apparatus is configured to be manipulated by a patient whilethe patient performs one or more treatment sessions. The patientinterface includes an output device and an input device. The inputdevice is configured to receive patient input correlating with at leastone pain level of the patient during the one or more treatment sessions.The computing device is configured to receive a treatment plan for thepatient. The treatment plan includes one or more exercise routines forthe patient to complete on the treatment apparatus during the one ormore treatment sessions. The computing device is further configured toreceive treatment data pertaining to the patient. The computing deviceis also configured to receive patient input from the patient interface.The computing device is further configured to use the treatment plan,the treatment data, and the patient input to generate at least onethreshold. Responsive to an occurrence of exceeding the at least onethreshold, the computing device is configured to modify the treatmentplan using an artificial intelligence engine.

Another aspect of the disclosed embodiments includes a method formodifying, by an artificial intelligence engine, a treatment plan foroptimizing patient outcome and pain levels during one or more treatmentsessions. The method includes receiving the treatment plan for apatient. The treatment plan includes one or more exercise routines forthe patient to complete during the one or more treatment sessions. Themethod also includes receiving treatment data pertaining to the patientand receiving patient input correlating with at least one of the painlevels of the patient. The method further includes using the treatmentplan, the treatment data, and the patient input to generate at least onethreshold. Responsive to an occurrence of exceeding the at least onethreshold, the method includes modifying the treatment plan.

Another aspect of the disclosed embodiments includes a tangible,non-transitory computer-readable medium storing instructions that, whenexecuted, cause a processing device to receive a treatment plan for apatient. The treatment plan includes one or more exercise routines forthe patient to complete during one or more treatment sessions. Theinstructions also cause the processing device to receive treatment datapertaining to the patient and receive patient input correlating with atleast one pain level of the patient during the one or more treatmentsessions. The instructions further cause the processing device to usethe treatment plan, the treatment data, and the patient input togenerate at least one threshold. Responsive to an occurrence ofexceeding the at least one threshold, the instructions cause theprocessing device to modify the treatment plan.

Another aspect of the disclosed embodiments includes a system formodifying, by an artificial intelligence engine, a treatment plan foroptimizing patient outcome and pain levels during one or more treatmentsessions. The system includes, in one example, a memory device and aprocessing device. The memory device stores instructions. The processingdevice is communicatively coupled to the memory device. The processingdevice executes the instructions to receive a treatment plan for apatient. The treatment plan includes one or more exercise routines forthe patient to complete during the one or more treatment sessions. Theprocessing device also executes the instructions to receive treatmentdata pertaining to the patient and receive patient input correlatingwith at least one of the pain levels of the patient. The processingdevice further executes the instructions to use the treatment plan, thetreatment data, and the patient input to generate at least onethreshold. Responsive to an occurrence of exceeding the at least onethreshold, the processing device executes the instructions to modify thetreatment plan.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure and its advantages,reference is now made to the following description, taken in conjunctionwith the accompanying drawings. It is emphasized that, according tocommon practice, the various features of the drawings are not to-scale.On the contrary, the dimensions of the various features are arbitrarilyexpanded or reduced for clarity.

FIG. 1 generally illustrates a block diagram of an embodiment of acomputer-implemented system for managing a treatment plan according toprinciples of the present disclosure.

FIG. 2 generally illustrates a perspective view of an embodiment of atreatment device according to principles of the present disclosure.

FIG. 3 generally illustrates a perspective view of an embodiment ofpedal of the treatment device of FIG. 2 according to principles of thepresent disclosure.

FIG. 4 generally illustrates a perspective view of a person using thetreatment device of FIG. 2 according to principles of the presentdisclosure.

FIG. 5 generally illustrates an example embodiment of an overviewdisplay of an assistant interface according to principles of the presentdisclosure.

FIG. 6 generally illustrates an example block diagram of training amachine learning model to output, based on data pertaining to thepatient, a treatment plan for the patient according to principles of thepresent disclosure.

FIG. 7 generally illustrates an example overview display of theassistant interface presenting recommended treatment plans and excludedtreatment plans in real-time during a telemedicine session according toprinciples of the present disclosure.

FIG. 8 generally illustrates an example overview display of theassistant interface presenting, in real-time during a telemedicinesession, recommended treatment plans that have changed as a result ofpatient data changing according to principles of the present disclosure.

FIG. 9 is a flow diagram generally illustrating a method for modifying,by an artificial intelligence engine, a treatment plan for optimizingpatient outcome and pain levels during one or more treatment sessionsaccording to principles of the present disclosure.

FIG. 10 is a flow diagram generally illustrating a method for furthermodifying a treatment plan for optimizing patient outcome and painlevels, by an artificial intelligence engine, during one or moretreatment sessions using updated pain levels according to principles ofthe present disclosure.

FIG. 11 generally illustrates a computer system according to principlesof the present disclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components.Different companies may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . .” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

The terminology used herein is for the purpose of describing particularexample embodiments only, and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

The terms first, second, third, etc. may be used herein to describevarious elements, components, regions, layers and/or sections; however,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer, or section from another region,layer, or section. Terms such as “first,” “second,” and other numericalterms, when used herein, do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer, or section discussed below could be termed a second element,component, region, layer, or section without departing from theteachings of the example embodiments. The phrase “at least one of,” whenused with a list of items, means that different combinations of one ormore of the listed items may be used, and only one item in the list maybe needed. For example, “at least one of: A, B, and C” includes any ofthe following combinations: A, B, C, A and B, A and C, B and C, and Aand B and C. In another example, the phrase “one or more” when used witha list of items means there may be one item or any suitable number ofitems exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” “top,” “bottom,” “inside,” “outside,”“contained within,” “superimposing upon,” and the like, may be usedherein. These spatially relative terms can be used for ease ofdescription to describe one element's or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms may also be intended to encompass differentorientations of the device in use, or operation, in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” the other elementsor features. Thus, the example term “below” can encompass both anorientation of above and below. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptions used herein interpreted accordingly.

A “treatment plan” may include one or more treatment protocols, and eachtreatment protocol includes one or more treatment sessions. Eachtreatment session comprises several session periods, with each sessionperiod including a particular exercise for treating the body part of thepatient. For example, a treatment plan for post-operative rehabilitationafter a knee surgery may include an initial treatment protocol withtwice daily stretching sessions for the first 3 days after surgery and amore intensive treatment protocol with active exercise sessionsperformed 4 times per day starting 4 days after surgery. A treatmentplan may also include information pertaining to a medical procedure toperform on the patient, a treatment protocol for the patient using atreatment device, a diet regimen for the patient, a medication regimenfor the patient, a sleep regimen for the patient, additional regimens,or some combination thereof.

The terms telemedicine, telehealth, telemed, teletherapeutic,telemedicine, etc. may be used interchangeably herein.

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of thepresent disclosure. Although one or more of these embodiments may bepreferred, the embodiments disclosed should not be interpreted, orotherwise used, as limiting the scope of the disclosure, including theclaims. In addition, one skilled in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

As patients engage in rehabilitation, their pain levels often increase.Patients may take more pain medication as their pain levels increase. Inaddition, the increased pain levels may discourage the patients fromdiligently following their rehabilitation treatment plans. Suchnoncompliance may slow down the recovery progresses of the patients,resulting with patient taking pain medication for longer periods oftime. As the quantity and the length of time (e.g., days, weeks, months,etc.) increases for which patients take their pain medication, the morelikely the patients will become addicted to and/or dependent on theirpain medication. For example, patients may become physically dependenton opioids and experience symptoms of tolerance (i.e., patients' bodiesbecome desensitized to the drugs and patients need to take higher dosesof the drug to relieve pain) and withdrawal (e.g., physical effects).Furthermore, patients may become mentally dependent on the opioids(i.e., the use of the opioids is a conditioned response to a feeling—atrigger—and the trigger sets off biochemical changes in the patients'brains that strongly influence addictive behavior). Prescribing anoptimal type and quantity of pain medication for an optimal length oftime can be challenging, especially when doctors are not provided withadequate patient input (e.g., a patient's pain level before, during, andafter a rehabilitation session) as the patient rehabilitates. It may bedesirable to modify a treatment plan for optimizing patient outcome andpain levels during one or more treatment sessions.

Determining optimal remote examination procedures to create a treatmentplan for a patient having certain characteristics (e.g., vital-sign orother measurements; performance; demographic; psychographic; geographic;diagnostic; measurement- or test-based; medically historic; behavioralhistoric; cognitive; etiologic; cohort-associative; differentiallydiagnostic; surgical, physically therapeutic, pharmacologic and othertreatment(s) recommended; etc.) may be a technically challengingproblem. For example, a multitude of information may be considered whendetermining a treatment plan, which may result in inefficiencies andinaccuracies in the treatment plan selection process. In arehabilitative setting, some of the multitude of information consideredmay include characteristics of the patient such as personal information,performance information, and measurement information. The personalinformation may include, e.g., demographic, psychographic or otherinformation, such as an age, a weight, a gender, a height, a body massindex, a medical condition, a familial medication history, an injury, amedical procedure, a medication prescribed, or some combination thereof.The performance information may include, e.g., an elapsed time of usinga treatment device, an amount of force exerted on a portion of thetreatment device, a range of motion achieved on the treatment device, amovement speed of a portion of the treatment device, an indication of aplurality of pain levels using the treatment device, or some combinationthereof. The measurement information may include, e.g., a vital sign, arespiration rate, a heartrate, a temperature, a blood pressure, or somecombination thereof. It may be desirable to process the characteristicsof a multitude of patients, the treatment plans performed for thosepatients, and the results of the treatment plans for those patients.

Doctors typically prescribe pain medication, such as opioids, to apatient after conducting a physical examination and/or communicatingwith the patient (e.g., to obtain the patient's rehabilitative progressand/or pain level). Another technical problem may involve distallytreating, via a computing device during a telemedicine or telehealthsession, a patient from a location different than a location at whichthe patient is located. An additional technical problem is controllingor enabling the control of, from the different location, a treatmentdevice used by the patient at the location at which the patient islocated. Oftentimes, when a patient undergoes rehabilitative surgery(e.g., knee surgery), a healthcare professional may prescribe atreatment device to the patient to use to perform a treatment protocolat their residence or any mobile location or temporary domicile. Ahealthcare professional may refer to a doctor, physician assistant,nurse, chiropractor, dentist, physical therapist, acupuncturist,physical trainer, coach, personal trainer, neurologist, cardiologist, orthe like. A healthcare profession may refer to any person with acredential, license, degree, or the like in the field of medicine,physical therapy, rehabilitation, or the like.

When the healthcare professional is located in a location different fromthe patient and the treatment device, it may be technically challengingfor the healthcare professional to monitor the patient's actual progress(as opposed to relying on the patient's word about the patient'sprogress) in using the treatment device, modify the treatment planaccording to the patient's progress, adapt the treatment device to thepersonal characteristics of the patient as the patient performs thetreatment plan, and the like.

Accordingly, systems and methods, such as those described herein, thatuse sensor data to modify a treatment plan and/or to adapt the treatmentdevice while a patient performs the treatment plan using the treatmentdevice, may be desirable.

In some embodiments, the systems and methods described herein may beconfigured to receive a treatment plan for a user, such as a patient.The treatment plan may correspond to a rehabilitation treatment plan, aprehabilitation treatment plan, an exercise treatment plan, or any othersuitable treatment plan. The treatment plan may comprise one or moreexercise routines for the patient to complete during one or moretreatment sessions. The patient may include a person performing the oneor more exercise routines. The person may perform the one or moreexercise routines on a treatment device, such as a rehabilitationdevice. The system and methods may be configured to receive treatmentdata pertaining to the patient. The treatment data may include variouscharacteristics of the patient, various measurement informationpertaining to the patient while the patient uses the treatment device,various characteristics of the treatment device, the treatment plan,other suitable data, or a combination thereof. The system and methodsmay be configured to receive patient input correlating with at least oneof the pain levels of the patient and use the treatment plan, thetreatment data, and the patient input to generate at least onethreshold. Responsive to an occurrence of exceeding the at least onethreshold, the systems and methods can modify the treatment plan.

In some embodiments, while the patient uses the treatment device toperform the treatment plan, at least some of the treatment data maycorrespond to sensor data of a sensor configured to sense variouscharacteristics of the treatment device and/or the measurementinformation of the patient. Additionally, or alternatively, while thepatient uses the treatment device to perform the treatment plan, atleast some of the treatment data may correspond to sensor data from asensor associated with a wearable device configured to sense themeasurement information of the patient.

The various characteristics of the treatment device may include one ormore settings of the treatment device, a current revolutions per timeperiod (e.g., such as one minute) of a rotating member (e.g., such as awheel) of the treatment device, a resistance setting of the treatmentdevice, other suitable characteristics of the treatment device, or acombination thereof. The measurement information may include one or morevital signs of the patient, a respiration rate of the patient, aheartrate of the patient, a temperature of the patient, a blood pressureof the patient, other suitable measurement information of the patient,or a combination thereof.

In some embodiments, the systems and methods described herein may beconfigured to generate treatment information using the treatment data.The treatment information may include a summary of the performance ofthe treatment plan by the patient while using the treatment device, suchthat the treatment data is presentable at a computing device of ahealthcare professional responsible for the performance of the treatmentplan by the patient. The healthcare professional may include a medicalprofessional (e.g., such as a doctor, a nurse, a therapist, and thelike), an exercise professional (e.g., such as a coach, a trainer, anutritionist, and the like), or another professional sharing at leastone of medical and exercise attributes (e.g., such as an exercisephysiologist, a physical therapist, an occupational therapist, and thelike). As used herein, and without limiting the foregoing, a “healthcareprofessional” may be a human being, a robot, a virtual assistant, avirtual assistant in a virtual and/or augmented reality, or anartificially intelligent entity, including a software program,integrated software and hardware, or hardware alone.

In some embodiments, the patient input may include a patient goal, alevel of exhaustion, pain level, or any other suitable information orcombination thereof. For example, the patient goal may include a targetrehabilitation date, a maximum or a minimum length of time for one ormore exercise sessions, a level of difficulty, or any other desiredgoal. The level of exhaustion may include the current level ofexhaustion of the patient (e.g., based on a scale of pain level valuesfrom 1-10), the number of hours the patient slept during the previousnight, or any other desired information. The pain level may include oneor more levels of pain (e.g., based on a scale of pain level values from1-10) the patient experiences before, during, and/or after the exercisesessions. The patient may input one or more pain level valuescorrelating with one or more body parts. For example, a patient may berehabilitating from a double knee surgery and each knee is recovering atdifferent paces. The patient may be experiencing a pain level value offour on the right knee and a pain level value of seven on the left kneeprior to an exercise session. The patient may experience an increase inpain levels during the exercise session (e.g., a pain level value offive on the right knee and a pain level value of nine on the left knee).

The threshold may include one or more threshold conditions. The one ormore threshold conditions may be based on characteristics of the injury,the patient, the treatment plan, the recovery results, the examinationresults, the pain level, the level of exhaustion, the exercise session,any other suitable factors, or combination thereof. For example, apatient may be using a treatment device, such as an exercise bicycle,during a treatment session. The threshold may include a thresholdcondition that the patient cannot apply more than first and secondamounts of measured force to right and left pedals, respectively. Thetreatment device may include one or more modes, such as anactive-assisted mode, that can assist a user in cycling. Theactive-assisted mode may refer to a sensor of the treatment devicereceiving measurements of revolutions per minute of one or moreradially-adjustable couplings, and causing the electric motor to drivethe one or more radially-adjustable couplings rotationally coupled tothe one or more pedals when the measured revolutions per minute satisfya parameter (e.g., a threshold condition). The threshold condition maybe configurable by the user and/or the physician, for example, as partof the treatment plan. The electric motor may be powered off while theuser provides the driving force to the radially-adjustable couplings aslong as the revolutions per minute are above a revolutions per minutethreshold and the threshold condition is not satisfied. When therevolutions per minute are less than the revolutions per minutethreshold then the threshold condition is satisfied and the electricmotor may be controlled to drive the radially-adjustable couplings tomaintain the revolutions per minute threshold.

Responsive to an occurrence of exceeding the at least one threshold, theartificial intelligence engine may be trained to modify the treatmentplan. Modifying the treatment plan may comprise generating at least oneupdated exercise routine during one of the one or more treatmentsessions. For example, if the patient's pain level exceeds a thresholdduring a treatment session, the artificial intelligence engine maygenerate an updated exercise routine. The updated exercise routine mayinclude changes, such as changes to an amount of time of the treatmentsession, an amount of time between treatment sessions (e.g., for thepatient to rest and for the patient's pain level to decrease), a type ofexercise to be completed in the treatment session, a type of treatmentdevice for the patient to perform on during the treatment session, anyother desired modification, or combination thereof. The updated exerciseroutine may include a changes to parameters of the treatment device,such as changes to a radius of one or more of the pedals, a level ofassistance applied by the electric motor to assist the patient withcycling, an amount of resistance the electric motor applies to the oneor more pedals, any other desired change to a parameter, or combinationthereof.

In some embodiments, the systems and methods can control the treatmentdevice while the patient uses the treatment device, including during atelemedicine session. The controlling can be based on parameters of themodified treatment plan. For example, the artificial intelligence enginemay be configured to modify the treatment plan such that the treatmentdevice changes a radius of rotation of one or more of the pedals, alevel of assistance applied by the electric motor to assist the patientwith cycling, an amount of resistance the electric motor applies to theone or more pedals, any other desired control, or combination thereof.

In some embodiments, the artificial intelligence engine can beconfigured to receive the modified patient input correlating with anupdated pain level of the patient (e.g., the patient inputs a change inpain level during a treatment session) and use the modified treatmentplan, the treatment data, and the modified patient input to generate atleast one modified threshold. Responsive to an occurrence of exceedingthe at least one modified threshold, the artificial intelligence enginecan be configured to modify the modified treatment plan. The modifiedtreatment plan can include one or more modifications that differ fromthe treatment plan. For example, the modified treatment plan may differfrom the treatment plan by having one or more different exerciseroutines for the patient to perform on a treatment device, one or moredifferent parameters for controlling the treatment device, one or moredifferent thresholds, any other differences, or combinations thereof.

In some embodiments, at least one of the treatment data and the patientinput can be received in real-time or near real-time and the treatmentplan can be modified in real-time or near real-time. The In someembodiments, at least one of the modified patient input and the modifiedpatient input can be received in real-time or near real-time and themodified treatment plan can be modified in real-time or near real-time.

In some embodiments, the healthcare professional may review thetreatment information and determine whether to modify the treatment planand/or one or more characteristics of the treatment device. For example,the healthcare professional may review the treatment information andcompare the treatment information to the treatment plan being performedby the patient.

Depending on what result is desired, the artificial intelligence enginemay be trained to output several treatment plans. For example, oneresult may include recovering to a threshold level (e.g., 75% range ofmotion) in a fastest amount of time, while another result may includefully recovering (e.g., 100% range of motion) regardless of the amountof time. Another result may include recovering while not exceeding athreshold level for pain (e.g., at or below a specific pain level)between treatment sessions, while another result may include recoveringwhile not exceeding a threshold level for pain during a treatmentsession.

The artificial intelligence engine may compare the following (i)expected information, which pertains to the patient while the patientuses the treatment device to perform the treatment plan to (ii) themeasurement information (e.g., indicated by the treatment information),which pertains to the patient while the patient uses the treatmentdevice to perform the treatment plan. The expected information mayinclude one or more vital signs of the patient, a respiration rate ofthe patient, a heartrate of the patient, a temperature of the patient, ablood pressure of the patient, other suitable information of thepatient, or a combination thereof. The artificial intelligence enginemay determine that the treatment plan is optimal for the particularpatient (i.e., the patient is having a desired rehabilitation result) ifone or more parts or portions of the measurement information are withinan acceptable range associated with one or more corresponding parts orportions of the expected information (e.g., within one or morethresholds). Conversely, the artificial intelligence engine maydetermine that the treatment plan is not optimal for the particularpatient (i.e., the patient is not having a desired rehabilitationresult) if one or more parts or portions of the measurement informationare outside of the range associated with one or more corresponding partsor portions of the expected information (e.g., outside of the one ormore thresholds).

For example, the artificial intelligence engine may determine whether ablood pressure value (e.g., systolic pressure, diastolic pressure,and/or pulse pressure) corresponding to the patient while the patientuses the treatment device (e.g., indicated by the measurementinformation) is within an acceptable range (e.g., plus or minus 1%, plusor minus 5%, or any suitable range) of an expected blood pressure valueindicated by the expected information. The artificial intelligenceengine may determine that the treatment plan is having the desiredeffect if the blood pressure value corresponding to the patient whilethe patient uses the treatment device is within the range of theexpected blood pressure value. Conversely, the artificial intelligenceengine may determine that the treatment plan is not having the desiredeffect if the blood pressure value corresponding to the patient whilethe patient uses the treatment device is outside of the range of theexpected blood pressure value.

In another example, the artificial intelligence engine may determinewhether a pain level corresponding to the patient while the patient usesthe treatment device (e.g., indicated by the patient input) is within anacceptable range of an expected pain level value indicated by theexpected information (e.g., a pain level value for a patient two daysafter surgery is expected to be higher than the pain level value of thepatient two weeks after the surgery, a pain level value for a patienthaving right ankle surgery is expected to be higher during recovery thanan uninjured left ankle). The artificial intelligence engine maydetermine that the treatment plan is having the desired effect if thepain level corresponding to the patient while the patient uses thetreatment device is within the range of the expected pain level value(e.g., not exceeding the threshold). Conversely, the artificialintelligence engine may determine that the treatment plan is not havingthe desired effect if the pain level value corresponding to the patientwhile the patient uses the treatment device is outside of the range ofthe expected pain level value. If the artificial intelligence enginedetermines that an occurrence of exceeding the at least one thresholdoccurs, then the artificial intelligence engine may be configured tomodify the treatment plan or any previously modified treatment plans.

In some embodiments, the artificial intelligence engine may compare theexpected characteristics of the treatment device while the patient usesthe treatment device to perform the treatment plan with characteristicsof the treatment device indicated by the treatment information. Forexample, the artificial intelligence engine may compare an expectedresistance setting of the treatment device with an actual resistancesetting of the treatment device indicated by the treatment information.The artificial intelligence engine may determine that the user isperforming the treatment plan properly if the actual characteristics ofthe treatment device indicated by the treatment information are within arange of corresponding ones of the expected characteristics of thetreatment device. Conversely, the artificial intelligence engine maydetermine that the user is not performing the treatment plan properly ifthe actual characteristics of the treatment device indicated by thetreatment information are outside the range of corresponding ones of theexpected characteristics of the treatment device. If the artificialintelligence engine determines that an occurrence of exceeding the atleast one threshold occurs, then the artificial intelligence engine maybe configured to modify the treatment plan or any previously modifiedtreatment plans.

If the artificial intelligence engine determines that the treatmentinformation indicates that the user is performing the treatment planproperly and/or that the treatment plan is having the desired effect,the artificial intelligence engine may determine not to modify thetreatment plan or the one or more characteristics of the treatmentdevice. Conversely, while the patient uses the treatment device toperform the treatment plan, if the artificial intelligence enginedetermines that the treatment information indicates that the patient isnot or has not been performing the treatment plan properly and/or thatthe treatment plan is not or has not been having the desired effect, theartificial intelligence engine may determine to modify the treatmentplan and/or the one or more characteristics of the treatment device.

In some embodiments, the system may interact with a user interface toprovide treatment plan input indicating one or more modifications to thetreatment plan and/or to one or more characteristics of the treatmentdevice if the artificial intelligence engine determines to modify thetreatment plan and/or the one or more characteristics of the treatmentdevice. For example, the interface may provide input indicating anincrease or decrease in the resistance setting of the treatment device,an increase or decrease in an amount of time the user is required to usethe treatment device according to the treatment plan, or other suitablemodification to the one or more characteristics of the treatment device.

In some embodiments, the systems and methods described herein may beconfigured to modify the treatment plan based on one or moremodifications indicated by the treatment plan input. Additionally, oralternatively, the systems and methods described herein may beconfigured to modify the one or more characteristics of the treatmentdevice based on the modified treatment plan and/or the treatment planinput. For example, the treatment plan input may indicate to modify theone or more characteristics of the treatment device and/or the modifiedtreatment plan may require or indicate adjustments to the treatmentdevice in order for the user to achieve the desired results of themodified treatment plan.

In some embodiments, the systems and methods described herein may beconfigured to receive subsequent treatment data pertaining to the userwhile the user uses the treatment device to perform the modifiedtreatment plan. For example, after the artificial intelligence enginemodifies the treatment plan and/or controls the one or morecharacteristics of the treatment device, the user may continue use thetreatment device to perform the modified treatment plan. The subsequenttreatment data may correspond to treatment data generated while the useruses the treatment device to perform the modified treatment plan. Insome embodiments, the subsequent treatment data may correspond totreatment data generated while the user continues to use the treatmentdevice to perform the treatment plan, after the healthcare professionalhas received the treatment information and determined not to modify thetreatment plan and/or control the one or more characteristics of thetreatment device.

Based on subsequent (e.g., modified) treatment plan input generated bythe artificial intelligence engine, the systems and methods describedherein may be configured to further modify the treatment plan and/orcontrol the one or more characteristics of the treatment device. Thesubsequent treatment plan input may correspond to input provided by thepatient at the user interface, from treatment data corresponding tosensor data from a sensor of a wearable device worn by the patientduring one of the one or more treatment sessions, from a sensorconfigured to detect treatment data pertaining to the patient, any otherdesired information, or combination thereof.

The healthcare professional may receive and/or review treatmentinformation continuously or periodically while the user uses thetreatment device to perform the treatment plan. Based on one or moretrends indicated by the continuously and/or periodically receivedtreatment information, the healthcare professional may determine whetherto modify the treatment plan and/or control the one or morecharacteristics of the treatment device. For example, the one or moretrends may indicate an increase in heart rate or other suitable trendsindicating that the user is not performing the treatment plan properlyand/or performance of the treatment plan by the person is not having thedesired effect.

In some embodiments, the systems and methods described herein may beconfigured to use artificial intelligence and/or machine learning toassign patients to cohorts and to dynamically control a treatment devicebased on the assignment during an adaptive telemedicine session. In someembodiments, numerous treatment devices may be provided to patients. Thetreatment devices may be used by the patients to perform treatment plansin their residences, at a gym, at a rehabilitative center, at ahospital, or any suitable location, including permanent or temporarydomiciles.

In some embodiments, the treatment devices may be communicativelycoupled to a server. Characteristics of the patients, including thetreatment data, may be collected before, during, and/or after thepatients perform the treatment plans. For example, the personalinformation, the performance information, and the measurementinformation may be collected before, during, and/or after the patientperforms the treatment plans. The results (e.g., improved performance ordecreased performance) of performing each exercise may be collected fromthe treatment device throughout the treatment plan and after thetreatment plan is performed. The parameters, settings, configurations,etc. (e.g., position of pedal, amount of resistance, etc.) of thetreatment device may be collected before, during, and/or after thetreatment plan is performed.

Each characteristic of the patient, each result, and each parameter,setting, configuration, etc. may be timestamped and may be correlatedwith a particular step in the treatment plan. Such a technique mayenable determining which steps in the treatment plan lead to desiredresults (e.g., improved muscle strength, range of motion, etc.) andwhich steps lead to diminishing returns (e.g., continuing to exerciseafter 3 minutes actually delays or harms recovery).

Data may be collected from the treatment devices and/or any suitablecomputing device (e.g., computing devices where personal information isentered, such as the interface of the computing device described herein,a clinician interface, patient interface, and the like) over time as thepatients use the treatment devices to perform the various treatmentplans. The data that may be collected may include the characteristics ofthe patients, the treatment plans performed by the patients, the resultsof the treatment plans, any of the data described herein, any othersuitable data, or a combination thereof.

In some embodiments, the data may be processed to group certain patientsinto cohorts. The patients may be grouped by patients having certain orselected similar characteristics, treatment plans, and results ofperforming the treatment plans. For example, athletic patients having nomedical conditions who perform a treatment plan (e.g., use the treatmentdevice for 30 minutes a day 5 times a week for 3 weeks) and who fullyrecover may be grouped into a first cohort. Older patients who areclassified obese and who perform a treatment plan (e.g., use thetreatment plan for 10 minutes a day 3 times a week for 4 weeks) and whoimprove their range of motion by 75 percent may be grouped into a secondcohort.

In some embodiments, an artificial intelligence engine may include oneor more machine learning models that are trained using the cohorts. Forexample, the one or more machine learning models may be trained toreceive an input of characteristics of a new patient and to output atreatment plan for the patient that results in a desired result. Themachine learning models may match a pattern between the characteristicsof the new patient and at least one patient of the patients included ina particular cohort. When a pattern is matched, the machine learningmodels may assign the new patient to the particular cohort and selectthe treatment plan associated with the at least one patient. Theartificial intelligence engine may be configured to control, distallyand based on the treatment plan, the treatment device while the newpatient uses the treatment device to perform the treatment plan.

As may be appreciated, the characteristics of the new patient (e.g., anew user) may change as the new patient uses the treatment device toperform the treatment plan. For example, the performance of the patientmay improve quicker than expected for patients in the cohort to whichthe new patient is currently assigned. Accordingly, the machine learningmodels may be trained to dynamically reassign, based on the changedcharacteristics, the new patient to a different cohort that includespatients having characteristics similar to the now-changedcharacteristics as the new patient. For example, a clinically obesepatient may lose weight and no longer meet the weight criterion for theinitial cohort, result in the patient's being reassigned to a differentcohort with a different weight criterion.

A different treatment plan may be selected for the new patient, and thetreatment device may be controlled, distally (e.g., which may bereferred to as remotely) and based on the different treatment plan, thetreatment device while the new patient uses the treatment device toperform the treatment plan. Such techniques may provide the technicalsolution of distally controlling a treatment device.

Further, the systems and methods described herein may lead to fasterrecovery times and/or better results for the patients because thetreatment plan that most accurately fits their characteristics isselected and implemented, in real-time, at any given moment. “Real-time”may also refer to near real-time, which may be less than 10 seconds orany reasonably proximate difference between two times. As describedherein, the term “results” may refer to medical results or medicaloutcomes. Results and outcomes may refer to responses to medicalactions. The term “medical action(s)” may refer to any suitableaction(s) performed by the healthcare professional, and such action oractions may include diagnoses, prescriptions for treatment plans,prescriptions for treatment devices, and the making, composing and/orexecuting of appointments, telemedicine sessions, prescription ofmedicines, telephone calls, emails, text messages, and the like.

Further, the artificial intelligence engine may be trained to outputtreatment plans that are not optimal i.e., sub-optimal, nonstandard, orotherwise excluded (all referred to, without limitation, as “excludedtreatment plans”) for the patient. For example, if a patient has highblood pressure, a particular exercise may not be approved or suitablefor the patient as it may put the patient at unnecessary risk or eveninduce a hypertensive crisis and, accordingly, that exercise may beflagged in the excluded treatment plan for the patient. In someembodiments, the artificial intelligence engine may monitor thetreatment data received while the patient (e.g., the user) with, forexample, high blood pressure, uses the treatment device to perform anappropriate treatment plan and may modify the appropriate treatment planto include features of an excluded treatment plan that may providebeneficial results for the patient if the treatment data indicates thepatient is handling the appropriate treatment plan without aggravating,for example, the high blood pressure condition of the patient.

In some embodiments, the treatment plans and/or excluded treatment plansmay be presented, during a telemedicine or telehealth session, to ahealthcare professional. The healthcare professional may select aparticular treatment plan for the patient to cause that treatment planto be transmitted to the patient and/or to control, based on thetreatment plan, the treatment device. In some embodiments, to facilitatetelehealth or telemedicine applications, including remote diagnoses,determination of treatment plans and rehabilitative and/or pharmacologicprescriptions, the artificial intelligence engine may receive and/oroperate distally from the patient and the treatment device.

In such cases, the recommended treatment plans and/or excluded treatmentplans may be presented simultaneously with a video of the patient inreal-time or near real-time during a telemedicine or telehealth sessionon a user interface of a computing device of a healthcare professional.The video may also be accompanied by audio, text and other multimediainformation and/or sensorial or perceptive (e.g., tactile, gustatory,haptic, pressure-sensing-based or electromagnetic (e.g.,neurostimulation)). Real-time may refer to less than or equal to 2seconds. Near real-time may refer to any interaction of a sufficientlyshort time to enable two individuals to engage in a dialogue via suchuser interface, and will generally be less than 10 seconds (or anysuitable proximate difference between two different times) but greaterthan 2 seconds.

Presenting the treatment plans generated by the artificial intelligenceengine concurrently with a presentation of the patient video may providean enhanced user interface because the healthcare professional maycontinue to visually and/or otherwise communicate with the patient whilealso reviewing the treatment plans on the same user interface. Theenhanced user interface may improve the healthcare professional'sexperience using the computing device and may encourage the healthcareprofessional to reuse the user interface. Such a technique may alsoreduce computing resources (e.g., processing, memory, network) becausethe healthcare professional does not have to switch to another userinterface screen to enter a query for a treatment plan to recommendbased on the characteristics of the patient. The artificial intelligenceengine may be configured to provide, dynamically on the fly, thetreatment plans and excluded treatment plans.

In some embodiments, the treatment device may be adaptive and/orpersonalized because its properties, configurations, and positions maybe adapted to the needs of a particular patient. For example, the pedalsmay be dynamically adjusted on the fly (e.g., via a telemedicine sessionor based on programmed configurations in response to certainmeasurements being detected) to increase or decrease a range of motionto comply with a treatment plan designed for the user. In someembodiments, a healthcare professional may adapt, remotely during atelemedicine session, the treatment device to the needs of the patientby causing a control instruction to be transmitted from a server totreatment device. Such adaptive nature may improve the results ofrecovery for a patient, furthering the goals of personalized medicine,and enabling personalization of the treatment plan on a per-individualbasis.

FIG. 1 shows a block diagram of a computer-implemented system 10,hereinafter called “the system” for managing a treatment plan. Managingthe treatment plan may include using an artificial intelligence engineto recommend treatment plans and/or provide excluded treatment plansthat should not be recommended to a patient.

The system 10 also includes a server 30 configured to store and toprovide data related to managing the treatment plan. The server 30 mayinclude one or more computers and may take the form of a distributedand/or virtualized computer or computers. The server 30 may also includea first communication interface 32 configured to communicate with theclinician interface 20 via a first network 34. In some embodiments, thefirst network 34 may include wired and/or wireless network connectionssuch as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC),cellular data networks, etc. The server 30 may include a first processor36 and a first machine-readable storage memory 38 (the latter of whichmay be called a “memory” for short), holding first instructions 40 forperforming the various actions of the server 30 for execution by thefirst processor 36. The server 30 may be configured to store dataregarding the treatment plan. For example, the memory 38 includes asystem data store 42 configured to hold system data, such as datapertaining to treatment plans for treating one or more patients. Theserver 30 may also be configured to store data regarding performance bya patient in following a treatment plan. For example, the memory 38includes a patient data store 44 configured to hold patient data, suchas data pertaining to the one or more patients, including datarepresenting each patient's performance within the treatment plan.

In addition, the characteristics (e.g., personal, performance,measurement, etc.) of the patients, the treatment plans followed by thepatients, the level of compliance with the treatment plans, and theresults of the treatment plans may use correlations and otherstatistical or probabilistic measures to enable the partitioning of orto partition the treatment plans into different patientcohort-equivalent databases in the patient data store 44. For example,the data for a first cohort of first patients having a first similarinjury, a first similar medical condition, a first similar medicalprocedure performed, a first treatment plan followed by the firstpatient, and a first result of the treatment plan may be stored in afirst patient database. The data for a second cohort of second patientshaving a second similar injury, a second similar medical condition, asecond similar medical procedure performed, a second treatment planfollowed by the second patient, and a second result of the treatmentplan may be stored in a second patient database. Any singlecharacteristic or any combination of characteristics may be used toseparate the cohorts of patients. In some embodiments, the differentcohorts of patients may be stored in different partitions or volumes ofthe same database. There is no specific limit to the number of differentcohorts of patients allowed, other than as limited by mathematicalcombinatoric and/or partition theory.

This characteristic data, treatment plan data, and results data may beobtained from numerous treatment apparatuses and/or computing devicesover time and stored in the database 44. The characteristic data,treatment plan data, and results data may be correlated in thepatient-cohort databases in the patient data store 44. Thecharacteristics of the patients may include personal information,performance information, and/or measurement information.

In addition to the historical information about other patients stored inthe patient cohort-equivalent databases, real-time or near-real-timeinformation based on the current patient's characteristics about acurrent patient being treated may be stored in an appropriate patientcohort-equivalent database. The characteristics of the patient may bedetermined to match or be similar to the characteristics of anotherpatient in a particular cohort (e.g., cohort A) and the patient may beassigned to that cohort.

In some embodiments, the server 30 may execute an artificialintelligence (AI) engine 11 that uses one or more machine learningmodels 13 to perform at least one of the embodiments disclosed herein.The server 30 may include a training engine 9 capable of generating theone or more machine learning models 13. The machine learning models 13may be trained to assign patients to certain cohorts based on theircharacteristics, select treatment plans using real-time and historicaldata correlations involving patient cohort-equivalents, and control atreatment apparatus 70, among other things. The one or more machinelearning models 13 may be generated by the training engine 9 and may beimplemented in computer instructions executable by one or moreprocessing devices of the training engine 9 and/or the servers 30. Togenerate the one or more machine learning models 13, the training engine9 may train the one or more machine learning models 13. The one or moremachine learning models 13 may be used by the artificial intelligenceengine 11.

The training engine 9 may be a rackmount server, a router computer, apersonal computer, a portable digital assistant, a smartphone, a laptopcomputer, a tablet computer, a netbook, a desktop computer, an Internetof Things (IoT) device, any other desired computing device, or anycombination of the above. The training engine 9 may be cloud-based, areal-time software platform, or an embedded system (e.g.,microcode-based and/or implemented) and it may include privacy softwareor protocols, and/or security software or protocols.

To train the one or more machine learning models 13, the training engine9 may use a training data set of a corpus of the characteristics of thepatients that used the treatment apparatus 70 to perform treatmentplans, the details (e.g., treatment protocol including exercises, amountof time to perform the exercises, how often to perform the exercises, aschedule of exercises, parameters/configurations/settings of thetreatment apparatus 70 throughout each step of the treatment plan, etc.)of the treatment plans performed by the patients using the treatmentapparatus 70, and the results of the treatment plans performed by thepatients. The one or more machine learning models 13 may be trained tomatch patterns of characteristics of a patient with characteristics ofother patients in assigned to a particular cohort. The term “match” mayrefer to an exact match, a correlative match, a substantial match, etc.The one or more machine learning models 13 may be trained to receive thecharacteristics of a patient as input, map the characteristics tocharacteristics of patients assigned to a cohort, and select a treatmentplan from that cohort. The one or more machine learning models 13 mayalso be trained to control, based on the treatment plan, the treatmentapparatus 70.

Different machine learning models 13 may be trained to recommenddifferent treatment plans for different desired results. For example,one machine learning model may be trained to recommend treatment plansfor most effective recovery, while another machine learning model may betrained to recommend treatment plans based on speed of recovery.

Using training data that includes training inputs and correspondingtarget outputs, the one or more machine learning models 13 may refer tomodel artifacts created by the training engine 9. The training engine 9may find patterns in the training data wherein such patterns map thetraining input to the target output, and generate the machine learningmodels 13 that capture these patterns. In some embodiments, theartificial intelligence engine 11, the database 33, and/or the trainingengine 9 may reside on another component (e.g., assistant interface 94,clinician interface 20, etc.) depicted in FIG. 1.

The one or more machine learning models 13 may comprise, e.g., a singlelevel of linear or non-linear operations (e.g., a support vector machine[SVM]) or the machine learning models 13 may be a deep network, i.e., amachine learning model comprising multiple levels of non-linearoperations. Examples of deep networks are neural networks includinggenerative adversarial networks, convolutional neural networks,recurrent neural networks with one or more hidden layers, and fullyconnected neural networks (e.g., each neuron may transmit its outputsignal to the input of the remaining neurons, as well as to itself). Forexample, the machine learning model may include numerous layers and/orhidden layers that perform calculations (e.g., dot products) usingvarious neurons.

The system 10 also includes a patient interface 50 configured tocommunicate information to a patient and to receive feedback from thepatient. Specifically, the patient interface includes an input device 52and an output device 54, which may be collectively called a patient userinterface 52, 54. The input device 52 may include one or more devices,such as a keyboard, a mouse, a touch screen input, a gesture sensor,and/or a microphone and processor configured for voice recognition. Theoutput device 54 may take one or more different forms including, forexample, a computer monitor or display screen on a tablet, smartphone,or a smart watch. The output device 54 may include other hardware and/orsoftware components such as a projector, virtual reality capability,augmented reality capability, etc. The output device 54 may incorporatevarious different visual, audio, or other presentation technologies. Forexample, the output device 54 may include a non-visual display, such asan audio signal, which may include spoken language and/or other soundssuch as tones, chimes, and/or melodies, which may signal differentconditions and/or directions. The output device 54 may comprise one ormore different display screens presenting various data and/or interfacesor controls for use by the patient. The output device 54 may includegraphics, which may be presented by a web-based interface and/or by acomputer program or application (App.).

As shown in FIG. 1, the patient interface 50 includes a secondcommunication interface 56 (one example of an “input device”, an “outputdevice,” or both), which may also be called a remote communicationinterface configured to communicate with the server 30 and/or theclinician interface 20 via a second network 58. In some embodiments, thesecond network 58 may include a local area network (LAN), such as anEthernet network. In some embodiments, the second network 58 may includethe Internet, and communications between the patient interface 50 andthe server 30 and/or the clinician interface 20 may be secured viaencryption, such as, for example, by using a virtual private network(VPN). In some embodiments, the second network 58 may include wiredand/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee,Near-Field Communications (NFC), cellular data networks, etc. In someembodiments, the second network 58 may be the same as and/oroperationally coupled to the first network 34.

The patient interface 50 may include a second processor 60 and a secondmachine-readable storage memory 62 holding second instructions 64 forexecution by the second processor 60 for performing various actions ofpatient interface 50. The second machine-readable storage memory 62 alsoincludes a local data store 66 configured to hold data, such as datapertaining to a treatment plan and/or patient data, such as datarepresenting a patient's performance within a treatment plan. Thepatient interface 50 also includes a local communication interface 68configured to communicate with various devices for use by the patient inthe vicinity of the patient interface 50. The local communicationinterface 68 (one example of an “input device,” an “output device,” orboth) may include wired and/or wireless communications. In someembodiments, the local communication interface 68 may include a localwireless network such as Wi-Fi, Bluetooth, ZigBee, Near-FieldCommunications (NFC), cellular data networks, etc.

The system 10 also includes a treatment apparatus 70 configured to bemanipulated by the patient and/or to manipulate a body part of thepatient for performing activities according to the treatment plan. Insome embodiments, the treatment apparatus 70 may take the form of anexercise and rehabilitation apparatus configured to perform and/or toaid in the performance of a rehabilitation regimen, which may be anorthopedic rehabilitation regimen, and the treatment includesrehabilitation of a body part of the patient, such as a joint or a boneor a muscle group. The treatment apparatus 70 (one example of a“treatment device”) may be any suitable medical, rehabilitative,therapeutic, etc. apparatus configured to be controlled distally viaanother computing device to treat a patient and/or exercise the patient.The treatment apparatus 70 may be an electromechanical machine includingone or more weights, an electromechanical bicycle, an electromechanicalspin-wheel, a smart-mirror, a treadmill, or the like. The body part mayinclude, for example, a spine, a hand, a foot, a knee, or a shoulder.The body part may include a part of a joint, a bone, or a muscle group,such as one or more vertebrae, a tendon, or a ligament. As shown in FIG.1, the treatment apparatus 70 includes a controller 72, which mayinclude one or more processors, computer memory, and/or othercomponents. The treatment apparatus 70 may also include a fourthcommunication interface 74 configured to communicate with the patientinterface 50 via the local communication interface 68. The treatmentapparatus 70 may also include one or more internal sensors 76 and anactuator 78, such as a motor. The actuator 78 may be used, for example,for moving the patient's body part and/or for resisting forces by thepatient.

The internal sensors 76 may measure one or more operatingcharacteristics of the treatment apparatus 70 such as, for example, aforce, a position, a speed, and/or a velocity. In some embodiments, theinternal sensors 76 may include a position sensor configured to measureat least one of a linear motion or an angular motion of a body part ofthe patient. For example, an internal sensor 76 in the form of aposition sensor may measure a distance that the patient is able to movea part of the treatment apparatus 70, where such distance may correspondto or translate into a range of motion that the patient's body part isable to achieve. In some embodiments, the internal sensors 76 mayinclude a force sensor configured to measure a force applied by thepatient. For example, an internal sensor 76 in the form of a forcesensor may measure a force or weight the patient, using a particularbody part, is able to apply to the treatment apparatus 70.

The system 10 shown in FIG. 1 also includes an ambulation sensor 82,which communicates with the server 30 via the local communicationinterface 68 of the patient interface 50. The ambulation sensor 82 maytrack and store a number of steps taken by the patient. In someembodiments, the ambulation sensor 82 may take the form of a wristband,wristwatch, or smart watch. In some embodiments, the ambulation sensor82 may be integrated within a phone, such as a smartphone.

The system 10 shown in FIG. 1 also includes a goniometer 84, whichcommunicates with the server 30 via the local communication interface 68of the patient interface 50. The goniometer 84 measures an angle of thepatient's body part. For example, the goniometer 84 may measure theangle of flex of a patient's knee or elbow or shoulder.

The system 10 shown in FIG. 1 also includes a pressure sensor 86, whichcommunicates with the server 30 via the local communication interface 68of the patient interface 50. The pressure sensor 86 measures an amountof pressure or weight applied by a body part of the patient. Forexample, pressure sensor 86 may measure an amount of force applied by apatient's foot when pedaling a stationary bike

The system 10 shown in FIG. 1 also includes a supervisory interface 90which may be similar or identical to the clinician interface 20. In someembodiments, the supervisory interface 90 may have enhancedfunctionality beyond what is provided on the clinician interface 20. Thesupervisory interface 90 may be configured for use by a person havingresponsibility for the treatment plan, such as an orthopedic surgeon.

The system 10 shown in FIG. 1 also includes a reporting interface 92which may be similar or identical to the clinician interface 20. In someembodiments, the reporting interface 92 may have less functionality thanthe clinician interface 20. For example, the reporting interface 92 maynot have the ability to modify a treatment plan. Such a reportinginterface 92 may be used, for example, by a biller to determine the useof the system 10 for billing purposes. In another example, the reportinginterface 92 may not have the ability to display patient identifiableinformation, presenting only pseudonymized data and/or anonymized datafor certain data fields concerning a data subject and/or for certaindata fields concerning a quasi-identifier of the data subject. Such areporting interface 92 may be used, for example, by a researcher todetermine various effects of a treatment plan on different patients.

The system 10 includes an assistant interface 94 for an assistant, suchas a doctor, a nurse, a physical therapist, a technician, or ahealthcare professional, to remotely communicate with the patientinterface 50 and/or the treatment apparatus 70. Such remotecommunications may enable the assistant to provide assistance orguidance to a patient using the system 10. More specifically, theassistant interface 94 is configured to communicate a telemedicinesignal 96, 97, 98 a, 98 b, 99 a, 99 b with the patient interface 50 viaa network connection such as, for example, via the first network 34and/or the second network 58. The telemedicine signal 96, 97, 98 a, 98b, 99 a, 99 b comprises one of an audio signal 96, an audiovisual signal97, an interface control signal 98 a for controlling a function of thepatient interface 50, an interface monitor signal 98 b for monitoring astatus of the patient interface 50, an apparatus control signal 99 a forchanging an operating parameter of the treatment apparatus 70, and/or anapparatus monitor signal 99 b for monitoring a status of the treatmentapparatus 70. In some embodiments, each of the control signals 98 a, 99a may be unidirectional, conveying commands from the assistant interface94 to the patient interface 50. In some embodiments, in response tosuccessfully receiving a control signal 98 a, 99 a and/or to communicatesuccessful and/or unsuccessful implementation of the requested controlaction, an acknowledgement message may be sent from the patientinterface 50 to the assistant interface 94. In some embodiments, each ofthe monitor signals 98 b, 99 b may be unidirectional, status-informationcommands from the patient interface 50 to the assistant interface 94. Insome embodiments, an acknowledgement message may be sent from theassistant interface 94 to the patient interface 50 in response tosuccessfully receiving one of the monitor signals 98 b, 99 b.

In some embodiments, the patient interface 50 may be configured as apass-through for the apparatus control signals 99 a and the apparatusmonitor signals 99 b between the treatment apparatus 70 and one or moreother devices, such as the assistant interface 94 and/or the server 30.For example, the patient interface 50 may be configured to transmit anapparatus control signal 99 a in response to an apparatus control signal99 a within the telemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b fromthe assistant interface 94.

In some embodiments, the assistant interface 94 may be presented on ashared physical device as the clinician interface 20. For example, theclinician interface 20 may include one or more screens that implementthe assistant interface 94. Alternatively or additionally, the clinicianinterface 20 may include additional hardware components, such as a videocamera, a speaker, and/or a microphone, to implement aspects of theassistant interface 94.

In some embodiments, one or more portions of the telemedicine signal 96,97, 98 a, 98 b, 99 a, 99 b may be generated from a prerecorded source(e.g., an audio recording, a video recording, or an animation) forpresentation by the output device 54 of the patient interface 50. Forexample, a tutorial video may be streamed from the server 30 andpresented upon the patient interface 50. Content from the prerecordedsource may be requested by the patient via the patient interface 50.Alternatively, via a control on the assistant interface 94, theassistant may cause content from the prerecorded source to be played onthe patient interface 50.

The assistant interface 94 includes an assistant input device 22 and anassistant display 24, which may be collectively called an assistant userinterface 22, 24. The assistant input device 22 may include one or moreof a telephone, a keyboard, a mouse, a trackpad, or a touch screen, forexample. Alternatively or additionally, the assistant input device 22may include one or more microphones. In some embodiments, the one ormore microphones may take the form of a telephone handset, headset, orwide-area microphone or microphones configured for the assistant tospeak to a patient via the patient interface 50. In some embodiments,assistant input device 22 may be configured to provide voice-basedfunctionalities, with hardware and/or software configured to interpretspoken instructions by the assistant by using the one or moremicrophones. The assistant input device 22 may include functionalityprovided by or similar to existing voice-based assistants such as Siriby Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung. Theassistant input device 22 may include other hardware and/or softwarecomponents. The assistant input device 22 may include one or moregeneral purpose devices and/or special-purpose devices.

The assistant display 24 may take one or more different forms including,for example, a computer monitor or display screen on a tablet, asmartphone, or a smart watch. The assistant display 24 may include otherhardware and/or software components such as projectors, virtual realitycapabilities, or augmented reality capabilities, etc. The assistantdisplay 24 may incorporate various different visual, audio, or otherpresentation technologies. For example, the assistant display 24 mayinclude a non-visual display, such as an audio signal, which may includespoken language and/or other sounds such as tones, chimes, melodies,and/or compositions, which may signal different conditions and/ordirections. The assistant display 24 may comprise one or more differentdisplay screens presenting various data and/or interfaces or controlsfor use by the assistant. The assistant display 24 may include graphics,which may be presented by a web-based interface and/or by a computerprogram or application (App.).

In some embodiments, the system 10 may provide computer translation oflanguage from the assistant interface 94 to the patient interface 50and/or vice-versa. The computer translation of language may includecomputer translation of spoken language and/or computer translation oftext, wherein the text and/or spoken language may be any language,formal or informal, current or outdated, digital, quantum or analog,invented, human or animal (e.g., dolphin) or ancient, with respect tothe foregoing, e.g., Old English, Zulu, French, Japanese, Klingon,Kobaïan, Attic Greek, Modern Greek, etc., and in any form, e.g.,academic, dialectical, patois, informal, e.g., “electronic texting,”etc. Additionally or alternatively, the system 10 may provide voicerecognition and/or spoken pronunciation of text. For example, the system10 may convert spoken words to printed text and/or the system 10 mayaudibly speak language from printed text. The system 10 may beconfigured to recognize spoken words by any or all of the patient, theclinician, and/or the assistant. In some embodiments, the system 10 maybe configured to recognize and react to spoken requests or commands bythe patient. For example, the system 10 may automatically initiate atelemedicine session in response to a verbal command by the patient(which may be given in any one of several different languages).

In some embodiments, the server 30 may generate aspects of the assistantdisplay 24 for presentation by the assistant interface 94. For example,the server 30 may include a web server configured to generate thedisplay screens for presentation upon the assistant display 24. Forexample, the artificial intelligence engine 11 may generate recommendedtreatment plans and/or excluded treatment plans for patients andgenerate the display screens including those recommended treatment plansand/or external treatment plans for presentation on the assistantdisplay 24 of the assistant interface 94. In some embodiments, theassistant display 24 may be configured to present a virtualized desktophosted by the server 30. In some embodiments, the server 30 may beconfigured to communicate with the assistant interface 94 via the firstnetwork 34. In some embodiments, the first network 34 may include alocal area network (LAN), such as an Ethernet network. In someembodiments, the first network 34 may include the Internet, andcommunications between the server 30 and the assistant interface 94 maybe secured via privacy enhancing technologies, such as, for example, byusing encryption over a virtual private network (VPN). Alternatively oradditionally, the server 30 may be configured to communicate with theassistant interface 94 via one or more networks independent of the firstnetwork 34 and/or other communication means, such as a direct wired orwireless communication channel. In some embodiments, the patientinterface 50 and the treatment apparatus 70 may each operate from apatient location geographically separate from a location of theassistant interface 94. For example, the patient interface 50 and thetreatment apparatus 70 may be used as part of an in-home rehabilitationsystem, which may be aided remotely by using the assistant interface 94at a centralized location, such as a clinic or a call center.

In some embodiments, the assistant interface 94 may be one of severaldifferent terminals (e.g., computing devices) that may be physically,virtually or electronically grouped together, for example, in one ormore call centers or at one or more clinicians' offices. In someembodiments, a plurality of assistant interfaces 94 may be distributedgeographically. In some embodiments, a person may work as an assistantremotely from any conventional office infrastructure, including a homeoffice. Such remote work may be performed, for example, where theassistant interface 94 takes the form of a computer and/or telephone.This remote work functionality may allow for work-from-home arrangementsthat may include full-time, part-time and/or flexible work hours for anassistant.

FIGS. 2-3 show an embodiment of a treatment apparatus 70. Morespecifically, FIG. 2 shows a treatment apparatus 70 in the form of astationary cycling machine 100, which may be called a stationary bike,for short. The stationary cycling machine 100 includes a set of pedals102 each attached to a pedal arm 104 for rotation about an axle 106. Insome embodiments, and as shown in FIG. 2, the pedals 102 are movable onthe pedal arms 104 in order to adjust a range of motion used by thepatient in pedaling. For example, the pedals being located inwardlytoward the axle 106 corresponds to a smaller range of motion than whenthe pedals are located outwardly away from the axle 106. A pressuresensor 86 is attached to or embedded within one of the pedals 102 formeasuring an amount of force applied by the patient on the pedal 102.The pressure sensor 86 may communicate wirelessly to the treatmentapparatus 70 and/or to the patient interface 50.

FIG. 4 shows a person (a patient) using the treatment apparatus of FIG.2, and showing sensors and various data parameters connected to apatient interface 50. The example patient interface 50 may be a tabletcomputer or smartphone, or a phablet, such as an iPad, an iPhone, anAndroid device, a Surface tablet, or any other electronic device heldmanually by the patient. In some other embodiments, the patientinterface 50 may be embedded within or attached to the treatmentapparatus 70, obviating the need for the patient to hold the devicemanually, other than for the possible purpose of interacting with it.FIG. 4 shows the patient wearing the ambulation sensor 82 on his wrist,with a note showing “STEPS TODAY 1355”, indicating that the ambulationsensor 82 has recorded and transmitted that step count to the patientinterface 50. FIG. 4 also shows the patient wearing the goniometer 84 onhis right knee, with a note showing “KNEE ANGLE 72°”, indicating thatthe goniometer 84 is measuring and transmitting that knee angle to thepatient interface 50. FIG. 4 also shows a right side of one of thepedals 102 with a pressure sensor 86 showing “FORCE 12.5 lbs.”,indicating that the right pedal pressure sensor 86 is measuring andtransmitting that force measurement to the patient interface 50. FIG. 4also shows a left side of one of the pedals 102 with a pressure sensor86 showing “FORCE 27 lbs.”, indicating that the left pedal pressuresensor 86 is measuring and transmitting that force measurement to thepatient interface 50. FIG. 4 also shows other patient data, such as anindicator of “SESSION TIME 0:04:13”, indicating that the patient hasbeen using the treatment apparatus 70 for 4 minutes and 13 seconds. Thissession time may be determined by the patient interface 50 based oninformation received from the treatment apparatus 70. FIG. 4 also showsan indicator showing “PAIN LEVEL 3”. Such a pain level may be obtainedfrom the patent in response to a solicitation or inquiry, such as aquestion, presented upon the patient interface 50.

FIG. 5 is an example embodiment of an overview display 120 of theassistant interface 94. Specifically, the overview display 120 presentsseveral different controls and interfaces for the assistant to remotelyassist a patient with using the patient interface 50 and/or thetreatment apparatus 70. This remote assistance functionality maycomprise a type of functionality present in telemedicine systems.

Specifically, the overview display 120 includes a patient profiledisplay 130 presenting biographical information regarding a patientusing the treatment apparatus 70. The patient profile display 130 maytake the form of a portion or region of the overview display 120, asshown in FIG. 5, although the patient profile display 130 may take otherforms, such as a separate screen or a popup window. In some embodiments,the patient profile display 130 may include a limited subset of thepatient's biographical information, health-related information, or both.More specifically, the data presented upon the patient profile display130 may depend upon the assistant's need for that information. Forexample, a healthcare professional assisting the patient with a medicalissue may be provided with medical history information regarding thepatient, whereas a technician troubleshooting an issue with thetreatment apparatus 70 may be provided with a much more limited set ofinformation regarding the patient. The technician, for example, may begiven only the patient's name. The patient profile display 130 mayinclude pseudonymized data and/or anonymized data or use any privacyenhancing technology to prevent confidential patient data from beingcommunicated in a way that could violate patient confidentialityrequirements. Such privacy enhancing technologies may enable compliancewith laws, regulations, or other rules of governance such as, but notlimited to, the Health Insurance Portability and Accountability Act(HIPAA), or the General Data Protection Regulation (GDPR), wherein thepatient may be deemed a “data subject.”

In some embodiments, the patient profile display 130 may presentinformation regarding the treatment plan for the patient to follow inusing the treatment apparatus 70. Such treatment plan information may belimited to an assistant who is a healthcare professional, such as adoctor or physical therapist. For example, a healthcare professionalassisting the patient with an issue regarding the treatment regimen maybe provided with treatment plan information, whereas a techniciantroubleshooting an issue with the treatment apparatus 70 may not beprovided with any information regarding the patient's treatment plan.

In some embodiments, one or more recommended treatment plans and/orexcluded treatment plans may be presented in the patient profile display130 to the assistant. The one or more recommended treatment plans and/orexcluded treatment plans may be generated by the artificial intelligenceengine 11 of the server 30 and received from the server 30 in real-timeduring, inter alia, a telemedicine session. An example of presenting theone or more recommended treatment plans and/or ruled-out treatment plansis described below with reference to FIG. 7.

The example overview display 120 shown in FIG. 5 also includes a patientstatus display 134 presenting status information regarding a patientusing the treatment apparatus. The patient status display 134 may takethe form of a portion or region of the overview display 120, as shown inFIG. 5, although the patient status display 134 may take other forms,such as a separate screen or a popup window. The patient status display134 includes sensor data 136 from one or more of the external sensors82, 84, 86, and/or from one or more internal sensors 76 of the treatmentapparatus 70. In some embodiments, the patient status display 134 maypresent other data 138 regarding the patient, such as last reported painlevel, or progress within a treatment plan.

User access controls may be used to limit access, including which datais available to be viewed and/or modified, on any or all of the userinterfaces 20, 50, 90, 92, and 94 of the system 10. In some embodiments,user access controls may be employed to control which information isavailable to any given person, wherein the given person is using thesystem 10. For example, data presented on the assistant interface 94 maybe controlled by user access controls, with permissions set depending onthe assistant/user's need for and/or qualifications to view thatinformation.

The example overview display 120 shown in FIG. 5 also includes a helpdata display 140 presenting information for the assistant to use inassisting the patient. The help data display 140 may take the form of aportion or region of the overview display 120, as shown in FIG. 5. Thehelp data display 140 may take other forms, such as a separate screen ora popup window. The help data display 140 may include, for example,presenting answers to frequently asked questions regarding use of thepatient interface 50 and/or the treatment apparatus 70. The help datadisplay 140 may also include research data or best practices. In someembodiments, the help data display 140 may present scripts for answersor explanations in response to patient questions. In some embodiments,the help data display 140 may present flow charts or walk-throughs forthe assistant to use in determining a root cause and/or solution to apatient's problem. In some embodiments, the assistant interface 94 maypresent two or more help data displays 140, which may be the same ordifferent, for simultaneous presentation of help data for use by theassistant. For example, a first help data display may be used to presenta troubleshooting flowchart to determine the source of a patient'sproblem, and a second help data display may present script informationfor the assistant to read to the patient, such information to preferablyinclude directions for the patient to perform some action, which mayhelp to narrow down or solve the problem. In some embodiments, basedupon inputs to the troubleshooting flowchart in the first help datadisplay, the second help data display may automatically populate withscript information.

The example overview display 120 shown in FIG. 5 also includes a patientinterface control 150 presenting information regarding the patientinterface 50, and/or for modifying one or more settings of the patientinterface 50. The patient interface control 150 may take the form of aportion or region of the overview display 120, as shown in FIG. 5. Thepatient interface control 150 may take other forms, such as a separatescreen or a popup window. The patient interface control 150 may presentinformation communicated to the assistant interface 94 via one or moreof the interface monitor signals 98 b. As shown in FIG. 5, the patientinterface control 150 includes a display feed 152 of the displaypresented by the patient interface 50. In some embodiments, the displayfeed 152 may include a live copy of the display screen currently beingpresented to the patient by the patient interface 50. In other words,the display feed 152 may present an image of what is presented on adisplay screen of the patient interface 50. In some embodiments, thedisplay feed 152 may include abbreviated information regarding thedisplay screen currently being presented by the patient interface 50,such as a screen name or a screen number. The patient interface control150 may include a patient interface setting control 154 for theassistant to adjust or to control one or more settings or aspects of thepatient interface 50. In some embodiments, the patient interface settingcontrol 154 may cause the assistant interface 94 to generate and/or totransmit an interface control signal 98 for controlling a function or asetting of the patient interface 50.

In some embodiments, the patient interface setting control 154 mayinclude a collaborative browsing or co-browsing capability for theassistant to remotely view and/or control the patient interface 50. Forexample, the patient interface setting control 154 may enable theassistant to remotely enter text to one or more text entry fields on thepatient interface 50 and/or to remotely control a cursor on the patientinterface 50 using a mouse or touchscreen of the assistant interface 94.

In some embodiments, using the patient interface 50, the patientinterface setting control 154 may allow the assistant to change asetting that cannot be changed by the patient. For example, the patientinterface 50 may be precluded from enabling access to a language settingin order to prevent a patient from inadvertently switching, on thepatient interface 50, the language used for the displays, whereas thepatient interface setting control 154 may enable the assistant to changethe language setting of the patient interface 50. In another example,the patient interface 50 may not be able to change a font size settingto a smaller size in order to prevent a patient from inadvertentlyswitching the font size used for the displays on the patient interface50 such that the display would become illegible or unintelligible to thepatient, whereas the patient interface setting control 154 may providefor the assistant to change the font size setting of the patientinterface 50.

The example overview display 120 shown in FIG. 5 also includes aninterface communications display 156 showing the status ofcommunications between the patient interface 50 and one or more otherdevices 70, 82, 84, such as the treatment apparatus 70, the ambulationsensor 82, and/or the goniometer 84. The interface communicationsdisplay 156 may take the form of a portion or region of the overviewdisplay 120, as shown in FIG. 5. The interface communications display156 may take other forms, such as a separate screen or a popup window.The interface communications display 156 may include controls for theassistant to remotely modify communications with one or more of theother devices 70, 82, 84. For example, the assistant may remotelycommand the patient interface 50 to reset communications with one of theother devices 70, 82, 84, or to establish communications with a new orreplacement one of the other devices 70, 82, 84. This functionality maybe used, for example, where the patient has a problem with one of theother devices 70, 82, 84, or where the patient receives a new or areplacement one of the other devices 70, 82, 84.

The example overview display 120 shown in FIG. 5 also includes anapparatus control 160 for the assistant to view and/or to controlinformation regarding the treatment apparatus 70. The apparatus control160 may take the form of a portion or region of the overview display120, as shown in FIG. 5. The apparatus control 160 may take other forms,such as being enabled through or presented on a separate screen or apopup window. The apparatus control 160 may include an apparatus statusdisplay 162 with information regarding the current status of theapparatus. The apparatus status display 162 may present informationcommunicated to the assistant interface 94 via one or more of theapparatus monitor signals 99 b. The apparatus status display 162 mayindicate whether the treatment apparatus 70 is currently communicatingwith the patient interface 50. The apparatus status display 162 maypresent other current and/or historical information regarding the statusof the treatment apparatus 70.

The apparatus control 160 may include an apparatus setting control 164for the assistant to adjust or control one or more aspects of thetreatment apparatus 70. The apparatus setting control 164 may cause theassistant interface 94 to generate and/or to transmit an apparatuscontrol signal 99 for changing an operating parameter of the treatmentapparatus 70 (e.g., a pedal radius setting, a resistance setting, atarget RPM, etc.). The apparatus setting control 164 may include a modebutton 166 and a position control 168, which may be used in conjunctionfor the assistant to place an actuator 78 of the treatment apparatus 70in a manual mode, after which a setting, such as a position or a speedof the actuator 78, can be changed using the position control 168. Themode button 166 may provide for a setting, such as a position, to betoggled between automatic and manual modes. In some embodiments, one ormore settings may be adjustable at any time, but without a necessity ofhaving an associated auto/manual mode. In some embodiments, theassistant may change an operating parameter of the treatment apparatus70, such as a pedal radius setting, while the patient is actively usingthe treatment apparatus 70. Such “on the fly” adjustment may or may notbe available to the patient using the patient interface 50. In someembodiments, the apparatus setting control 164 may allow the assistantto change a setting that cannot be changed by the patient using thepatient interface 50. For example, the patient interface 50 may beprecluded from changing a preconfigured setting, such as a height or atilt setting of the treatment apparatus 70, whereas the apparatussetting control 164 may provide for the assistant to change the heightor tilt setting of the treatment apparatus 70.

The example overview display 120 shown in FIG. 5 also includes a patientcommunications control 170 for controlling an audio or an audiovisualcommunications session with the patient interface 50. The communicationssession with the patient interface 50 may comprise a live feed from theassistant interface 94 for presentation on or by the output device ofthe patient interface 50. The live feed may take the form of an audiofeed and/or a video feed. In some embodiments, the patient interface 50may be configured to provide two-way audio or audiovisual communicationswith a person using the assistant interface 94. Specifically, thecommunications session with the patient interface 50 may includebidirectional (two-way) video or audiovisual feeds, with each of thepatient interface 50 and the assistant interface 94 presenting video ofthe other one. In some embodiments, the patient interface 50 may presentvideo from the assistant interface 94, while the assistant interface 94presents only audio or the assistant interface 94 presents no live audioor visual signal from the patient interface 50. In some embodiments, theassistant interface 94 may present video from the patient interface 50,while the patient interface 50 presents only audio or the patientinterface 50 presents no live audio or visual signal from the assistantinterface 94.

In some embodiments, the audio or an audiovisual communications sessionwith the patient interface 50 may take place, at least in part, whilethe patient is performing the rehabilitation regimen upon the body part.The patient communications control 170 may take the form of a portion orregion of the overview display 120, as shown in FIG. 5. The patientcommunications control 170 may take other forms, such as being enabledby or on a separate screen or a popup window. The audio and/oraudiovisual communications may be processed and/or directed by theassistant interface 94 and/or by another device or devices, such as atelephone system, or a videoconferencing system (e.g., Zoom, WebEx,etc.) used by the assistant while the assistant uses the assistantinterface 94. Alternatively or additionally, the audio and/oraudiovisual communications may include communications with a thirdparty. For example, the system 10 may enable the assistant to initiate a3-way conversation with the patient and a subject matter expert, such asa healthcare professional or a specialist, regarding use of a particularpiece of hardware or software. The example patient communicationscontrol 170 shown in FIG. 5 includes call controls 172 for the assistantto use in managing various aspects of the audio or audiovisualcommunications with the patient. The call controls 172 include adisconnect button 174 for the assistant to end the audio or audiovisualcommunications session. The call controls 172 also include a mute button176 to temporarily mute or attenuate an audio or audiovisual signal fromthe assistant interface 94. In some embodiments, the call controls 172may include other features, such as a hold button (not shown). The callcontrols 172 may also include one or more record/playback controls 178,such as record, play, and pause buttons to control, with the patientinterface 50, recording and/or playback of audio and/or video from theteleconference session. The call controls 172 may also include a videofeed display 180 for presenting still and/or video images from thepatient interface 50, and a self-video display 182 for showing thecurrent image of the assistant using the assistant interface. Theself-video display 182 may be presented as a picture-in-picture (PiP)format, such PiP format being within a section of the video feed display180, as shown in FIG. 5. Alternatively or additionally, the self-videodisplay 182 may be presented separately and/or independently from thevideo feed display 180.

The example overview display 120 shown in FIG. 5 also includes a thirdparty communications control 190 for use in conducting audio and/oraudiovisual communications with a third party. The third partycommunications control 190 may take the form of a portion or region ofthe overview display 120, as shown in FIG. 5. The third partycommunications control 190 may take other forms, such as enabling orpresenting on a display on a separate screen or a popup window. Thethird party communications control 190 may include one or more controls,such as a contact list and/or buttons or controls to contact a thirdparty regarding use of a particular piece of hardware or software, e.g.,a subject matter expert, such as a healthcare professional or aspecialist. The third party communications control 190 may include aconference-calling capability for the third party to simultaneouslycommunicate with both the assistant via the assistant interface 94, andthe patient via the patient interface 50. For example, the system 10 mayprovide for the assistant to initiate a 3-way conversation with thepatient and the third party.

FIG. 6 shows an example block diagram of training a machine learningmodel 13 to output, based on data 600 pertaining to the patient, atreatment plan 602 for the patient according to the present disclosure.Data pertaining to other patients may be received by the server 30. Theother patients may have used various treatment apparatuses to performtreatment plans. The data may include characteristics of the otherpatients, the details of the treatment plans performed by the otherpatients, and/or the results of performing the treatment plans (e.g., apercentage of recovery of a portion of the patients' bodies, an amountof recovery of a portion of the patients' bodies, an amount of increaseor decrease in muscle strength of a portion of patients' bodies, anamount of increase or decrease in range of motion of a portion ofpatients' bodies, etc.).

As depicted in FIG. 6, the data has been assigned to different cohorts.Cohort A includes data for patients having similar firstcharacteristics, first treatment plans, and first results. Cohort Bincludes data for patients having similar second characteristics, secondtreatment plans, and second results. For example, cohort A may includefirst characteristics of patients in their twenties without any medicalconditions, and wherein such patients underwent surgery for a brokenlimb; their treatment plans may include a certain treatment protocol(e.g., use the treatment apparatus 70 for 30 minutes 5 times a week for3 weeks, wherein values for the properties, configurations, and/orsettings of the treatment apparatus 70 are set to X (where X is anumerical value) for the first two weeks and to Y (where Y is anumerical value) for the last week).

As further depicted in FIG. 6, Cohort A and cohort B may be included ina training dataset used to train the machine learning model 13. Themachine learning model 13 may be trained to match a pattern between oneor more characteristics for each cohort and output the treatment planthat provides the result, i.e., the best match. Accordingly, when thedata 600 for a new patient is input into the trained machine learningmodel 13, the trained machine learning model 13 may match the one ormore characteristics included in the data 600 with one or morecharacteristics in either cohort A or cohort B and output theappropriate treatment plan 602. In some embodiments, the machinelearning model 13 may be trained to output one or more excludedtreatment plans that should not be performed by the new patient.

FIG. 7 shows an embodiment of an overview display 120 of the assistantinterface 94 presenting in real-time during a telemedicine sessionrecommended treatment plans and excluded treatment plans according tothe present disclosure. As depicted in FIG. 7, the overview display 120just includes sections for the patient profile 130 and the video feeddisplay 180, including the self-video display 182. Any suitableconfiguration of controls and interfaces of the overview display 120described with reference to FIG. 5 may be presented in addition to orinstead of the patient profile 130, the video feed display 180, and theself-video display 182.

As further depicted in FIG. 7, the assistant (e.g., healthcareprofessional) using the assistant interface 94 (e.g., computing device)during the telemedicine session may be presented in the self-video 182in a portion of the overview display 120 (e.g., user interface presentedon a display screen 24 of the assistant interface 94) that also presentsa video from the patient in the video feed display 180. Further, thevideo feed display 180 may also include a graphical user interface (GUI)object 700 (e.g., a button) that enables the healthcare professional toshare on the patient interface 50, in real-time or near real-time duringthe telemedicine session, the recommended treatment plans and/or theexcluded treatment plans with the patient. The healthcare professionalmay select the GUI object 700 to share the recommended treatment plansand/or the excluded treatment plans. As depicted, another portion of theoverview display 120 includes the patient profile display 130.

The patient profile display 130 illustrated in FIG. 7 presents twoexample recommended treatment plans 600 and one example excludedtreatment plan 602. As described herein, the treatment plans may berecommended in view of characteristics of the patient being treated. Togenerate the recommended treatment plans 600 the patient should followto achieve a desired result, a pattern between the characteristics ofthe patient being treated and a cohort of other patients who have usedthe treatment apparatus 70 to perform a treatment plan may be matched byone or more machine learning models 13 of the artificial intelligenceengine 11. Each of the recommended treatment plans may be generatedbased on different desired results, i.e., different desired outcomes orbest matches.

For example, as depicted in FIG. 7, the patient profile display 130presents “The characteristics of the patient match characteristics ofpatients in Cohort A. The following treatment plans are recommended forthe patient based on his characteristics and desired results.” Then, thepatient profile display 130 presents recommended treatment plans fromcohort A, and each treatment plan provides different results.

As depicted in FIG. 7, treatment plan “1” indicates “Patient X shoulduse treatment apparatus for 30 minutes a day for 4 days to achieve anincreased range of motion of Y %; Patient X has Type 2 Diabetes; andPatient X should be prescribed medication Z for pain management duringthe treatment plan (medication Z is approved for patients having Type 2Diabetes).” Accordingly, the treatment plan generated achievesincreasing the range of motion of Y %. As may be appreciated, thetreatment plan also includes a recommended medication (e.g., medicationZ) to prescribe to the patient to manage pain in view of a known medicaldisease (e.g., Type 2 Diabetes) of the patient. That is, the recommendedpatient medication not only does not conflict with the medical conditionof the patient but thereby improves the probability of a superiorpatient outcome. This specific example and all such examples elsewhereherein are not intended to limit in any way the generated treatment planfrom recommending multiple medications, or from handling theacknowledgement, view, diagnosis and/or treatment of comorbid conditionsor diseases.

As illustrated in FIG. 7, recommended treatment plan “2” may specify,based on a different desired result of the treatment plan, a differenttreatment plan including a different treatment protocol for a treatmentapparatus, a different medication regimen, etc.

As depicted in FIG. 7, the patient profile display 130 may also presentthe excluded treatment plans 602. These types of treatment plans areshown to the assistant using the assistant interface 94 to alert theassistant not to recommend certain portions of a treatment plan to thepatient. For example, the excluded treatment plan could specify thefollowing: “Patient X should not use treatment apparatus for longer than30 minutes a day due to a heart condition; Patient X has Type 2Diabetes; and Patient X should not be prescribed medication M for painmanagement during the treatment plan (in this scenario, medication M cancause complications for patients having Type 2 Diabetes). Specifically,the excluded treatment plan points out a limitation of a treatmentprotocol where, due to a heart condition, Patient X should not exercisefor more than 30 minutes a day. The ruled-out treatment plan also pointsout that Patient X should not be prescribed medication M because itconflicts with the medical condition Type 2 Diabetes.

As further depicted in FIG. 7, the assistant may select the treatmentplan for the patient on the overview display 120. For example, theassistant may use an input peripheral (e.g., mouse, touchscreen,microphone, keyboard, etc.) to select from the treatment plans 600 forthe patient. In some embodiments, during the telemedicine session, theassistant may discuss the pros and cons of the recommended treatmentplans 600 with the patient.

In any event, the assistant may select, as depicted in FIG. 7, thetreatment plan for the patient to follow to achieve the desired result.The selected treatment plan may be transmitted to the patient interface50 for presentation. The patient may view the selected treatment plan onthe patient interface 50. In some embodiments, the assistant and thepatient may discuss during the telemedicine session the details (e.g.,treatment protocol using treatment apparatus 70, diet regimen,medication regimen, etc.) in real-time or in near real-time. In someembodiments, the server 30 may control, based on the selected treatmentplan and during the telemedicine session, the treatment apparatus 70 asthe user uses the treatment apparatus 70.

FIG. 8 shows an embodiment of the overview display 120 of the assistantinterface 94 presenting, in real-time during a telemedicine session,recommended treatment plans that have changed due to patient datachanging according to the present disclosure. As may be appreciated, thetreatment apparatus 70 and/or any computing device (e.g., patientinterface 50) may transmit data while the patient uses the treatmentapparatus 70 to perform a treatment plan. The data may include updatedcharacteristics of the patient. For example, the updated characteristicsmay include new performance information and/or measurement informationrelated to the patient, the apparatus, the environment, etc. Theperformance information may include a speed of a portion of thetreatment apparatus 70, a range of motion achieved by the patient, aforce exerted on a portion of the treatment apparatus 70, a heartrate ofthe patient, a blood pressure of the patient, a respiratory rate of thepatient, and so forth.

In one embodiment, the data received at the server 30 may be input intothe trained machine learning model 13, which may determine that thecharacteristics indicate the patient is on track to achieve one or moregoals associated with or part of the current treatment plan. Determiningthe patient is on track for the current treatment plan may cause thetrained machine learning model 13 to adjust a parameter of the treatmentapparatus 70. The adjustment may be based on a next step of thetreatment plan to further improve the performance of the patient duringthat next step so as to more quickly achieve the one or more goalsassociated with or part of the current treatment plan or to surpass saidone or more goals based on the adjustment.

In one embodiment, the data received at the server 30 may be input intothe trained machine learning model 13, which may determine that thecharacteristics indicate the patient is not on track (e.g., behindschedule, not able to maintain a speed, not able to achieve a certainrange of motion, is in too much pain, etc.) for the current treatmentplan or is ahead of schedule (e.g., exceeding a certain speed,exercising longer than specified with no pain, exerting more than aspecified force, etc.) for the current treatment plan. The trainedmachine learning model 13 may determine, due to the patient's not beingon track or being ahead of schedule, that the characteristics of thepatient no longer match the characteristics of the patients in thecohort to which the patient is assigned. Accordingly, the trainedmachine learning model 13 may reassign the patient to another cohortthat includes as qualifying characteristics the patient's then-currentcharacteristics. As such, the trained machine learning model 13 mayselect a new treatment plan from the new cohort and control, based onthe new treatment plan, the treatment apparatus 70. In some embodiments,the trained machine learning model 13 may directly control the treatmentapparatus 70 based on the new treatment plan. In other embodiments, thetrained machine learning model 13 may control the treatment apparatus 70based on the new treatment plan by updating one or more programs beingexecuted on the treatment apparatus 70 itself.

In some embodiments, prior to controlling the treatment apparatus 70,the server 30 may provide the new treatment plan 800 to the assistantinterface 94 for presentation in the patient profile 130. As depicted inFIG. 8, the patient profile 130 indicates “The characteristics of thepatient have changed and now match characteristics of patients in CohortB. The following treatment plan is recommended for the patient based onhis characteristics and desired results.” Then, the patient profile 130presents the new treatment plan 800 (“Patient X should use treatmentapparatus for 10 minutes a day for 3 days to achieve an increased rangeof motion of L %”). The assistant (healthcare professional) may selectthe new treatment plan 800, and the server 30 may receive the selection.The server 30 may control the treatment apparatus 70 based on the newtreatment plan 800. In some embodiments, the new treatment plan 800 maybe transmitted to the patient interface 50 such that the patient mayview the details of the new treatment plan 800.

FIG. 9 shows an example embodiment of a method 900 for modifying, by anartificial intelligence engine, a treatment plan for optimizing patientoutcome and pain levels during one or more treatment sessions accordingto the present disclosure. The method 900 is performed by processinglogic that may include hardware (circuitry, dedicated logic, etc.),software (such as is run on a general-purpose computer system or adedicated machine), or a combination of both. The method 900 and/or eachof its individual functions, routines, subroutines, or operations may beperformed by one or more processors of a computing device (e.g., anycomponent of FIG. 1, such as server 30 executing the artificialintelligence engine 11). In certain implementations, the method 900 maybe performed by a single processing thread. Alternatively, the method900 may be performed by two or more processing threads, each threadimplementing one or more individual functions, routines, subroutines, oroperations of the methods.

For simplicity of explanation, the method 900 is depicted in FIG. 9 anddescribed as a series of operations. However, operations in accordancewith this disclosure can occur in various orders and/or concurrently,and/or with other operations not presented and described herein. Forexample, the operations depicted in the method 900 in FIG. 9 may occurin combination with any other operation of any other method disclosedherein. Furthermore, not all illustrated operations may be required toimplement the method 900 in accordance with the disclosed subjectmatter. In addition, those skilled in the art will understand andappreciate that the method 900 could alternatively be represented as aseries of interrelated states via a state diagram or event diagram.

At block 902, the processing device may receive a treatment plan for apatient. The treatment plan may include one or more exercise routines.In some embodiments, the treatment plane may include one or moreexercise routines for the patient to perform on a treatment device ortreatment apparatus (e.g. on treatment apparatus 70).

At block 904, the processing device may receive treatment datapertaining to the patient. Treatment data may include, for example,characteristics of the patient, measurement information pertaining tothe patient while the patient uses the treatment device, characteristicsof the treatment device, the treatment plan, or a combination thereof.Characteristics of the patient may include, for example, age, healthhistory, fitness level, etc. The measurement information may include,for example, one or more vital signs of the user, a respiration rate ofthe user, a heartrate of the user, a temperature of the user, a bloodpressure of the user, other suitable measurement information of theuser, or a combination thereof. Various characteristics of the treatmentdevice may include, for example, one or more settings of the treatmentdevice, a current revolutions per minute of a rotating member (e.g.,such as a wheel) of the treatment device, a resistance setting of thetreatment device, an angular or rotational velocity of the treatmentdevice or components thereof, other suitable characteristics of thetreatment device, or a combination thereof.

At block 906, the processing device may receive patient inputcorrelating with at least one pain level of the patient. Patient inputmay also include, for example, a patient goal, an exhaustion level, etc.The patient may have different pain levels for different parts of thepatient's body (e.g., a double knee replacement wherein one knee isrecovering faster than the other).

At block 908, the processing device may use the treatment plan, thetreatment data, and the patient input to generate at least onethreshold. For example, the processing device may generate one or morethresholds described previously herein.

At block 910, responsive to an occurrence (e.g., detecting anoccurrence) of exceeding the at least one threshold, the treatment planmay be modified. For example, in some embodiments, at least one updatedexercise routine during one of the one or more treatment sessions isgenerated. Further, the treatment plan may be modified according to oneor more of the examples described previously herein.

In some embodiments, the processing device may control, based on themodified treatment plan, the treatment device while the patient uses thetreatment device (e.g., during a telemedicine session). For example, theprocessing device may cause the treatment device to modify at least oneof a volume, a pressure, a resistance, an angle, an angular orrotational velocity, a speed, and a time period. A patient's use of atreatment device may include uses in a clinician's office, in a physicaltherapy center, in a gym, in a home office, at home, in an exercise orother workout studio, or any of the foregoing when directed by aclinician or person distal to the location of the treatment device, suchas when the direction is for the purposes of telemedicine.

FIG. 10 shows an example embodiment of a method 1000 for furthermodifying the treatment plan, by an artificial intelligence engine,using one or more updated pain levels of the patient according to thepresent disclosure. The method 1000 includes operations performed byprocessors of a computing device (e.g., any component of FIG. 1, such asserver 30 executing the artificial intelligence engine 11). In someembodiments, one or more operations of the method 1000 are implementedin computer instructions stored on a memory device and executed by aprocessing device. The method 1000 may be performed in the same or asimilar manner as described above in regard to method 900. Theoperations of the method 1000 may be performed in some combination withany of the operations of any of the methods described herein. In someembodiments, the method 1000 may occur after block 910 in the method 900depicted in FIG. 9. That is, the method 1000 may occur after thetreatment plan is modified responsive to an occurrence of exceeding thedetermined threshold.

Regarding the method 1000, at block 1002, the processing device mayreceive modified patient input correlating with an updated pain level ofthe patient. At block 1004, the processing device may use the modifiedtreatment plan, the treatment data, and the modified patient input togenerate at least one modified threshold. At block 1006, responsive toan occurrence of exceeding the at least one modified threshold, themodified treatment plan may be modified (e.g., further modified). Insome embodiments, the difficulty of the treatment plan may be changedbased on the patient's pain level. For example, if the pain level is lowor nonexistent, then the difficulty of the workout may be increased forthe patient to recover faster. However, if there is too much pain, thenthe difficulty of the workout may be decreased to decrease the patient'spain level during the exercise session.

FIG. 11 shows an example computer system 1100 which can perform any oneor more of the methods described herein, in accordance with one or moreaspects of the present disclosure. In one example, computer system 1100may include a computing device and correspond to one or more of theassistance interface 94, reporting interface 92, supervisory interface90, clinician interface 20, server 30 (including the AI engine 11),patient interface 50, ambulatory sensor 82, goniometer 84, treatmentapparatus 70, pressure sensor 86, or any suitable component of FIG. 1.The computer system 1100 may be capable of executing instructionsimplementing the one or more machine learning models 13 of theartificial intelligence engine 11 of FIG. 1. The computer system may beconnected (e.g., networked) to other computer systems in a LAN, anintranet, an extranet, or the Internet, including via the cloud or apeer-to-peer network. The computer system may operate in the capacity ofa server in a client-server network environment. The computer system maybe a personal computer (PC), a tablet computer, a wearable (e.g.,wristband), a set-top box (STB), a personal Digital Assistant (PDA), amobile phone, a smartphone, a camera, a video camera, an Internet ofThings (IoT) device, or any device capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that device. Further, while only a single computer system isillustrated, the term “computer” shall also be taken to include anycollection of computers that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of the methodsdiscussed herein.

The computer system 1100 (one example of a “computing device”) includesa processing device 1102, a main memory 1104 (e.g., read-only memory(ROM), flash memory, solid state drives (SSDs), dynamic random accessmemory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 1106(e.g., flash memory, solid state drives (SSDs), static random accessmemory (SRAM)), and a data storage device 1108, which communicate witheach other via a bus 1110.

Processing device 1102 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1102 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1402 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a system on a chip, a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theprocessing device 1402 may be configured to execute instructions forperforming any of the operations and steps discussed herein.

The computer system 1100 may further include a network interface device1112. The computer system 1100 also may include a video display 1114(e.g., a liquid crystal display (LCD), a light-emitting diode (LED), anorganic light-emitting diode (OLED), a quantum LED, a cathode ray tube(CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), oneor more input devices 1116 (e.g., a keyboard and/or a mouse or agaming-like control), and one or more speakers 1118 (e.g., a speaker).In one illustrative example, the video display 1114 and the inputdevice(s) 1116 may be combined into a single component or device (e.g.,an LCD touch screen).

The data storage device 1116 may include a computer-readable medium 1120on which the instructions 1122 embodying any one or more of the methods,operations, or functions described herein is stored. The instructions1122 may also reside, completely or at least partially, within the mainmemory 1104 and/or within the processing device 1102 during executionthereof by the computer system 1100. As such, the main memory 1104 andthe processing device 1102 also constitute computer-readable media. Theinstructions 1122 may further be transmitted or received over a networkvia the network interface device 1112.

While the computer-readable storage medium 1120 is shown in theillustrative examples to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium capable of storing, encoding or carrying out a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

Any of the systems and methods described in this disclosure may be usedin connection with rehabilitation. Unless expressly stated otherwise, isto be understood that rehabilitation includes prehabilitation (alsoreferred to as “pre-habilitation” or “prehab”). Prehabilitation may beused as a preventative procedure or as a pre-surgical or pre-treatmentprocedure. Prehabilitation may include any action performed by or on apatient (or directed to be performed by or on a patient, including,without limitation, remotely or distally through telemedicine) to,without limitation, prevent or reduce a likelihood of injury (e.g.,prior to the occurrence of the injury); improve recovery time subsequentto surgery; improve strength subsequent to surgery; or any of theforegoing with respect to any non-surgical clinical treatment plan to beundertaken for the purpose of ameliorating or mitigating injury,dysfunction, or other negative consequence of surgical or non-surgicaltreatment on any external or internal part of a patient's body. Forexample, a mastectomy may require prehabilitation to strengthen musclesor muscle groups affected directly or indirectly by the mastectomy. As afurther non-limiting example, the removal of an intestinal tumor, therepair of a hernia, open-heart surgery or other procedures performed oninternal organs or structures, whether to repair those organs orstructures, to excise them or parts of them, to treat them, etc., canrequire cutting through and harming numerous muscles and muscle groupsin or about, without limitation, the abdomen, the ribs and/or thethoracic cavity. Prehabilitation can improve a patient's speed ofrecovery, measure of quality of life, level of pain, etc. in all theforegoing procedures. In one embodiment of prehabilitation, apre-surgical procedure or a pre-non-surgical-treatment may include oneor more sets of exercises for a patient to perform prior to suchprocedure or treatment. The patient may prepare an area of his or herbody for the surgical procedure by performing the one or more sets ofexercises, thereby strengthening muscle groups, improving existingand/or establishing new muscle memory, enhancing mobility, improvingblood flow, and/or the like.

In some embodiments, the systems and methods described herein may useartificial intelligence and/or machine learning to generate aprehabilitation treatment plan for a user. Additionally, oralternatively, the systems and methods described herein may useartificial intelligence and/or machine learning to recommend an optimalexercise machine configuration for a user. For example, a data model maybe trained on historical data such that the data model may be providedwith input data relating to the user and may generate output dataindicative of a recommended exercise machine configuration for aspecific user. Additionally, or alternatively, the systems and methodsdescribed herein may use machine learning and/or artificial intelligenceto generate other types of recommendations relating to prehabilitation,such as recommended reading material to educate the patient, arecommended medical professional specialist to contact, and/or the like.

Consistent with the above disclosure, the examples of systems andmethods enumerated in the following clauses are specificallycontemplated and are intended as a non-limiting set of examples.

Clause 1. A computer-implemented system, comprising: a treatmentapparatus configured to be manipulated by a patient while the patientperforms one or more treatment sessions; a patient interface comprisingan output device and an input device, the input device configured toreceive patient input correlating with at least one pain level of thepatient during the one or more treatment sessions; and a computingdevice configured to: receive a treatment plan for the patient, whereinthe treatment plan comprises one or more exercise routines for thepatient to complete on the treatment apparatus during the one or moretreatment sessions, receive treatment data pertaining to the patient,receive the patient input from the patient interface, use the treatmentplan, the treatment data, and the patient input to generate at least onethreshold, and responsive to an occurrence of exceeding the at least onethreshold, modify the treatment plan using an artificial intelligenceengine.

Clause 2. The computer-implemented system of any clause herein, whereinthe treatment data comprises at least one of characteristics of thepatient, measurement information pertaining to the patient while thepatient uses the treatment apparatus, and characteristics of thetreatment.

Clause 3. The computer-implemented system of any clause herein, whereinthe computing device is further configured to control, based on themodified treatment plan, the treatment apparatus while the patient usesthe treatment apparatus.

Clause 4. The computer-implemented system of any clause herein, whereinthe computing device is further configured to control, based on themodified treatment plan, the treatment apparatus while the patient usesthe treatment apparatus during a telemedicine session.

Clause 5. The computer-implemented system of any clause herein, whereinthe computing device is further configured to: receive modified patientinput correlating with an updated pain level of the patient; use themodified treatment plan, the treatment data, and the modified patientinput to generate at least one modified threshold; and responsive to anoccurrence of exceeding the at least one modified threshold, modify themodified treatment plan.

Clause 6. The computer-implemented system of any clause herein, wherein,to modify the treatment plan, the computing device is further configuredto generate at least one updated exercise routine during one of the oneor more treatment sessions.

Clause 7. A method for modifying, by an artificial intelligence engine,a treatment plan for optimizing patient outcome and pain levels duringone or more treatment sessions, the method comprising: receiving thetreatment plan for a patient, wherein the treatment plan comprises oneor more exercise routines for the patient to complete during the one ormore treatment sessions; receiving treatment data pertaining to thepatient; receiving patient input correlating with at least one of thepain levels of the patient; using the treatment plan, the treatmentdata, and the patient input to generate at least one threshold; andresponsive to an occurrence of exceeding the at least one threshold,modifying the treatment plan.

Clause 8. The method of any clause herein, wherein the treatment plancomprises the one or more exercise routines for the patient to performon a treatment device.

Clause 9. The method of any clause herein, wherein the treatment datacomprises at least one of characteristics of the patient, measurementinformation pertaining to the patient while the patient uses thetreatment device, and characteristics of the treatment device.

Clause 10. The method of any clause herein, further comprisingcontrolling, based on the modified treatment plan, the treatment devicewhile the patient uses the treatment device.

Clause 11. The method of any clause herein, further comprisingcontrolling, based on the modified treatment plan, the treatment devicewhile the patient uses the treatment device during a telemedicinesession.

Clause 12. The method of any clause herein, further comprising:receiving modified patient input correlating with an updated pain levelof the patient; using the modified treatment plan, the treatment data,and the modified patient input to generate at least one modifiedthreshold; and responsive to an occurrence of exceeding the at least onemodified threshold, modifying the modified treatment plan.

Clause 13. The method of any clause herein, wherein modifying thetreatment plan comprises generating at least one updated exerciseroutine during one of the one or more treatment sessions.

Clause 14. The method of any clause herein, wherein at least one of thetreatment data and the patient input is received in real-time or nearreal-time; and wherein the treatment plan is modified in real-time ornear real-time.

Clause 15. A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to: receivea treatment plan for a patient, wherein the treatment plan comprises oneor more exercise routines for the patient to complete during one or moretreatment sessions; receive treatment data pertaining to the patient;receive patient input correlating with at least one pain level of thepatient during the one or more treatment sessions; use the treatmentplan, the treatment data, and the patient input to generate at least onethreshold; and responsive to an occurrence of exceeding the at least onethreshold, modify the treatment plan.

Clause 16. The computer-readable medium of any clause herein, whereinthe treatment plan comprises the one or more exercise routines for thepatient to perform on a treatment device.

Clause 17. The computer-readable medium of any clause herein, whereinthe treatment data comprises at least one of characteristics of thepatient, measurement information pertaining to the patient while thepatient uses the treatment device, and characteristics of the treatmentdevice.

Clause 18. The computer-readable medium of any clause herein, whereinthe processing device is further configured to control, based on themodified treatment plan, the treatment device while the patient uses thetreatment device.

Clause 19. The computer-readable medium of any clause herein, whereinthe processing device is further configured to control, based on themodified treatment plan, the treatment device while the patient uses thetreatment device during a telemedicine session.

Clause 20. The computer-readable medium of claim any clause herein,wherein the processing device is further configured to: receive modifiedpatient input correlating with an updated pain level of the patient; usethe modified treatment plan, the treatment data, and the modifiedpatient input to generate at least one modified threshold; andresponsive to an occurrence of exceeding the at least one modifiedthreshold, modify the modified treatment plan.

Clause 21. The computer-readable medium of any clause herein, whereinmodifying the treatment plan comprises generating at least one updatedexercise routine during one of the one or more treatment sessions.

Clause 22. The computer-readable medium of any clause herein, wherein atleast one of the treatment data and the patient input is received inreal-time or near real-time; and wherein the treatment plan is modifiedin real-time or near real-time.

Clause 23. A system for modifying, by an artificial intelligence engine,a treatment plan for optimizing patient outcome and pain levels duringone or more treatment sessions, comprising: a memory device storinginstructions; and a processing device communicatively coupled to thememory device, the processing device executes the instructions to:receive the treatment plan for a patient, wherein the treatment plancomprises one or more exercise routines for the patient to completeduring the one or more treatment sessions; receive treatment datapertaining to the patient; receive patient input correlating with atleast one of the pain levels of the patient; use the treatment plan, thetreatment data, and the patient input to generate at least onethreshold; and responsive to an occurrence of exceeding the at least onethreshold, modify the treatment plan.

Clause 24. The system of any clause herein, wherein the treatment plancomprises the one or more exercise routines for the patient to performon a treatment device.

Clause 25. The system of any clause herein, wherein the treatment datacomprises at least one of characteristics of the patient, measurementinformation pertaining to the patient while the patient uses thetreatment device, and characteristics of the treatment device.

Clause 26. The system of any clause herein, wherein the processingdevice is further configured to control, based on the modified treatmentplan, the treatment device while the patient uses the treatment device.

Clause 27 The system of any clause herein, wherein the processing deviceis further configured to control, based on the modified treatment plan,the treatment device while the patient uses the treatment device duringa telemedicine session.

Clause 28. The system of any clause herein, wherein the processingdevice is further configured to: receive modified patient inputcorrelating with an updated pain level of the patient; use the modifiedtreatment plan, the treatment data, and the modified patient input togenerate at least one modified threshold; and responsive to anoccurrence of exceeding the at least one modified threshold, modify themodified treatment plan.

Clause 29. The system of any clause herein, wherein modifying thetreatment plan comprises generating at least one updated exerciseroutine during one of the one or more treatment sessions.

Clause 30. The system of any clause herein, wherein at least one of thetreatment data and the patient input is received in real-time or nearreal-time; and wherein the treatment plan is modified in real-time ornear real-time.

No part of the description in this application should be read asimplying that any particular element, step, or function is an essentialelement that must be included in the claim scope. The scope of patentedsubject matter is defined only by the claims. Moreover, none of theclaims is intended to invoke 25 U.S.C. § 104(f) unless the exact words“means for” are followed by a participle.

The foregoing description, for purposes of explanation, use specificnomenclature to provide a thorough understanding of the describedembodiments. However, it should be apparent to one skilled in the artthat the specific details are not required to practice the describedembodiments. Thus, the foregoing descriptions of specific embodimentsare presented for purposes of illustration and description. They are notintended to be exhaustive or to limit the described embodiments to theprecise forms disclosed. It should be apparent to one of ordinary skillin the art that many modifications and variations are possible in viewof the above teachings.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present invention. Once the above disclosureis fully appreciated, numerous variations and modifications will becomeapparent to those skilled in the art. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

What is claimed is:
 1. A computer-implemented system, comprising: aninterface comprising an output device and an input device, the inputdevice configured to receive user input correlating with at least onepain level of a user during one or more exercise sessions; and acomputing device configured to: receive data pertaining to the user,receive the user input from the interface, use a plan, the data, and theinput to generate at least one threshold, wherein the plan comprises oneor more exercise routines for the user to complete on an exercisemachine during the one or more exercise sessions, and responsive to anoccurrence of exceeding the at least one threshold, using an artificialintelligence engine to modify the plan.
 2. The computer-implementedsystem of claim 1, wherein the data comprises at least one ofcharacteristics of the user and measurement information pertaining tothe user while the user uses the exercise machine.
 3. Thecomputer-implemented system of claim 1, wherein the computing device isfurther configured to control, based on the modified plan, the exercisemachine while the patient uses the exercise machine.
 4. Thecomputer-implemented system of claim 1, wherein the computing device isfurther configured to control, based on the modified plan, the exercisemachine while the user uses the exercise machine during a telemedicinesession.
 5. The computer-implemented system of claim 1, wherein thecomputing device is further configured to: receive modified user inputfrom the interface correlating with an updated pain level of the user;use the modified plan, the data, and the modified input to generate atleast one modified threshold; and responsive to an occurrence ofexceeding the at least one modified threshold, modify the modified plan.6. The computer-implemented system of claim 1, wherein, to modify theplan, the computing device is further configured to generate at leastone updated exercise routine during one of the one or more exercisesessions.
 7. A method for modifying, by an artificial intelligenceengine, a plan for optimizing user outcome and pain levels during one ormore exercise sessions, the method comprising: receiving data pertainingto the user; receiving user input correlating with at least one of thepain levels of the user; using the plan, the data, and the user input togenerate at least one threshold, wherein the plan comprises one or moreexercise routines for the user to complete on an exercise machine duringthe one or more exercise sessions; and responsive to an occurrence ofexceeding the at least one threshold, using the artificial intelligenceengine to modify the plan.
 8. The method of claim 7, wherein the datacomprises at least one of characteristics of the user.
 9. The method ofclaim 8, wherein the data comprises at least one of the characteristicsof the user and measurement information pertaining to the user while theuser uses the exercise machine.
 10. The method of claim 7, furthercomprising controlling, based on the modified plan, the exercise machinewhile the user uses the exercise machine.
 11. The method of claim 7,further comprising controlling, based on the modified plan, the exercisemachine while the user uses the exercise machine during a telemedicinesession.
 12. The method of claim 7, further comprising: receivingmodified user input correlating with an updated pain level of the user;using the modified plan, the data, and the modified user input togenerate at least one modified threshold; and responsive to anoccurrence of exceeding the at least one modified threshold, using theartificial intelligence engine to modify the modified plan.
 13. Themethod of claim 7, wherein modifying the plan comprises generating atleast one updated exercise routine during one of the one or moreexercise sessions.
 14. The method of claim 7, wherein: at least one ofthe data and the user input is received in real-time or near real-time,and wherein the plan is modified in real-time or near real-time.
 15. Atangible, non-transitory computer-readable medium storing instructionsthat, when executed, cause a processing device to: receive datapertaining to a user; receive user input correlating with at least onepain level of the user during one or more exercise sessions; use a plan,the data, and the user input to generate at least one threshold, whereinthe plan comprises one or more exercise routines for the user tocomplete on an exercise machine during one or more exercise sessions;and responsive to an occurrence of exceeding the at least one threshold,use an artificial intelligence engine to modify the plan.
 16. Thecomputer-readable medium of claim 15, wherein the data comprises atleast one of characteristics of the user
 17. The computer-readablemedium of claim 16 wherein the data comprises at least the one ofcharacteristics of the user and measurement information pertaining tothe user while the user uses the exercise machine.
 18. Thecomputer-readable medium of claim 15, wherein the processing device isfurther configured to control, based on the modified plan, the exercisemachine while the user uses the exercise machine.
 19. Thecomputer-readable medium of claim 15, wherein the processing device isfurther configured to control, based on the modified plan, the exercisemachine while the user uses the exercise machine during a telemedicinesession.
 20. The computer-readable medium of claim 15, wherein theprocessing device is further configured to: receive modified user inputcorrelating with an updated pain level of the user; use the modifiedplan, the data, and the modified user input to generate at least onemodified threshold; and responsive to an occurrence of exceeding the atleast one modified threshold, use the artificial intelligence engine tomodify the modified treatment plan.